The DOI matches its Crossref record; 77 of the 78 references it cites were found in a registry or an index; 15 of the 44 sources its claims cite were checked for support.
Topology and data
Open accessAbstract
An important feature of modern science and engineering is that data of various kinds is being produced at an unprecedented rate. This is so in part because of new experimental methods, and in part because of the increase in the availability of high powered computing technology. It is also clear that the nature of the data we are obtaining is significantly different. For example, it is now often the case that we are given data in the form of very long vectors, where all but a few of the coordinates turn out to be irrelevant to the questions of interest, and further that we don’t necessarily know which coordinates are the interesting ones. A related fact is that the data is often very high-dimensional, which severely restricts our ability to visualize it. The data obtained is also often much noisier than in the past and has more missing information (missing data). This is particularly so in the case of biological data, particularly high throughput data from microarray or other sources. Our ability to analyze this data, both in terms of quantity and the nature of the data, is clearly not keeping pace with the data being produced. In this paper, we will discuss how geometry and topology can be applied to make useful contributions to the analysis of various kinds of data. Geometry and topology are very natural tools to apply in this direction, since geometry can be regarded as the study of distance functions, and what one often works with are distance functions on large finite sets of data. The mathematical formalism which has been developed for incorporating geometric and topological techniques deals with point clouds, i.e. finite sets of points equipped with a distance function. It then adapts tools from the various branches of geometry to the study of point clouds. The point clouds are intended to be thought of as finite samples taken from a geometric object, perhaps with noise. Here are some of the key points which come up when applying these geometric methods to data analysis. • Qualitative information is needed: One important goal of data analysis is to allow the user to obtain knowledge about the data, i.e. to understand how it is organized on a large scale. For example, if we imagine that we are looking at a data set constructed somehow from diabetes patients, it would be important to develop the understanding that there are two types of the disease, namely the juvenile and adult onset forms. Once that is established, one of course wants to develop quantitative methods for distinguishing them, but the first insight about the distinct forms of the disease is key.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
53 No basis given · 38 Own interpretation · 23 Own result · 17 No full text of the sources · 9 Supported · 5 Partly supported · 5 Sources not found · 1 Couldn’t confirm · 1 Not checked yet · accuracy not yet measured
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“Much of current research efforts are focused in this direction (see, e.g., [43] and [39]), and functoriality provides the right general mathematical framework for addressing them.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1. Introduction
What the sources say
- Could not verifycites [43] von Luxburg, Ulrike, 2008, Consistency of spectral clustering, Ann. Statist. sourceExperimental — accuracy not yet measured
The Introduction discusses questions about whether clustering algorithms converge, and Section 5.2 constructs operators for analyzing spectral clustering. Neither passage addresses functoriality or presents it as a framework for resolving ambiguities in clustering; searches for those concepts found no supporting passage in our copy. (LLM, conf=0.76)
- Not matchedcites [39] kleinbergJ.M. Kleinberg, An impossibility theorem for clustering, NIPS 2002: 446-453.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: unclear — It is unclear whether it could be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Could not verify
Every source the paper cites for this claim was checked and supports it.
In the paper
“We argue that, as suggested in [46], it has a role to play in the study of point cloud data as well, and we give two illustrations of how this could happen, within the context of clustering.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1. Introduction
What the sources say
- Supportedcites [46] McCullagh, Peter, 2002, What is a statistical model?, Ann. Statist. source
Section 8, “Extensive response variable” (page unavailable), describes a point process as a countable set of points and treats Poisson point-process models as natural transformations of functors. That supports the cited suggestion that functoriality could be useful for point-cloud data; the citing authors present clustering as their own proposed application. (LLM, conf=0.86)
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Niyogi, Smale, and Weinberger in [52] provide sufficient hypotheses which guarantee that this is possible for Riemannian manifolds.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.3. Persistent homology.
What the sources say
- Partly supportedcites [52] Niyogi, Partha, 2008, Finding the homology of submanifolds with high confidence from random samples, Discrete Comput. Geom. sourceExperimental — accuracy not yet measured
Section 2 (Preliminaries; page unavailable) considers compact Riemannian submanifolds sampled as data. Section 3 (An Outline of Our Main Results; page unavailable) gives conditions under which the samples recover the manifold’s homology with high confidence. It does not guarantee reconstruction of the manifold itself, so the claim is broader than the result. (LLM, conf=0.88)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“The result of this construction is a database M of ca.4.5 × 10 6 points on a 7-dimensional ellipsoid in R 8 .”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.4. Example: Natural image statistics.
What the sources say
- Partly supportedcites [41] lee A.B. Lee, K.S. Pedersen, and D. Mumford, The nonlinear statistics of high-contrast patches in natural images, International Journal of Computer Vision (54), No. 1-3, August 2003, pp. 83-103. sourceExperimental — accuracy not yet measured
Section 3 (Preprocessing; page unavailable) says the preprocessed points lie on a 7-dimensional ellipsoid in R⁹, which is transformed into a 7-dimensional sphere in R⁸. Section 2 (Optical and Range Data Sets; page unavailable) reports about 4.2 million optical patches, not approximately 4.5 million points in a database M. The source supports the general dimensional construction, but not the stated count and geometry together. (LLM, conf=0.85)
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Their method is to prove that the Čech complex associated to a covering by balls of a fixed radius is homotopy equivalent to the underlying manifold.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.3. Persistent homology.
What the sources say
- Supportedcites [52] Niyogi, Partha, 2008, Finding the homology of submanifolds with high confidence from random samples, Discrete Comput. Geom. source
Section 3 states that, when the sample is sufficiently dense and the ball radius meets the stated bound, the manifold is a deformation retract of the union of the balls. Section 3.1 constructs the nerve of that ball cover and invokes the Nerve Lemma because the balls are convex. Together, these support the claimed homotopy equivalence under the method’s conditions; the paper calls the nerve a simplicial complex rather than a Čech complex. (LLM, conf=0.88)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“However, it turns out that there is a classification theorem (see [64]) for a subcategory of the category of N-persistence F -vector spaces, where F is a field.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1=2
What the sources say
- Supportedcites [64] Zomorodian, Afra, 2005, Computing persistent homology, Discrete Comput. Geom. source
Section 3.1 (p. 11) establishes an equivalence between finite-type persistence modules and finitely generated graded modules over R[t]. Corollary 3.1 in Section 3.2 (p. 12) then classifies the isomorphism classes of finite-type persistence modules over a field F by finite sets of P-intervals. Finite-type modules are the subcategory referred to in the claim. (LLM, conf=0.94)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“In [9], such a context is developed which is not dependent on the choice of a cost function but is rather dependent on”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
What the sources say
- Supportedcites [9] memolicarlsson G. Carlsson and F. Memoli, Persistent Clustering and a Theorem of J. Kleinberg , Preprint, March 2008. source
The Discussion (p. 16) presents persistence and functoriality as organizing principles and proposes defining clustering methods through sets of constraints, rather than a chosen cost function. The Results (p. 10) explicitly compares the framework’s properties with Kleinberg’s axiomatic conditions. (LLM, conf=0.87)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“It can be shown that p Σ is a homotopy equivalence (see [65]).”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 4.3. Tree based persistence.
What the sources say
- Supportedcites [65] Zomorodian, Afra, 2008, Localized homology, Comput. Geom. source
Lemma 1 (global; page unavailable) states that projection from a blowup complex to the covered space is a homotopy equivalence when a simplicial complex is covered by subcomplexes. The paper uses π_X and X_U rather than pΣ and M(|Σ|,S), but states the projection result attributed to it. (LLM, conf=0.88)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“This notion of clustering is closely related to clique clustering algorithms in network clustering [53].”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
What the sources say
- Supportedcites [53] clique G. Palla, I. Derènyi, I. Farkas, and T. Vicsek, Uncovering the overlapping community structure of complex networks in nature and society, Nature, Volume 435, 9 June 2005, pp. 814-818. source
The paper defines network communities through connected sets of adjacent k-cliques (p. 2) and describes an algorithm that finds cliques and clusters them using a clique-overlap matrix (p. 3). It therefore supports the citation as an example of clique-based network clustering; the comparison to the citing paper’s equivalence relation E is the citing authors’ own framing. (LLM, conf=0.88)
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“There is a unique M gen -functorial persistent clustering algorithm Ξ which is scale invariant and which satisfies the requirement that Ξ(E) = P .”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
What the sources say
- Partly supportedcites [9] memolicarlsson G. Carlsson and F. Memoli, Persistent Clustering and a Theorem of J. Kleinberg , Preprint, March 2008. sourceExperimental — accuracy not yet measured
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
- It may state two separate claims as one. 39
- Probably not a substantial claim. 41
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Although clustering is clearly a very important part of data analysis, the ways in which it is formulated and implemented are fraught with ambiguities.In particular, the arbitrariness of various threshhold choices and lack of robustness are difficulties one confronts.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1. Introduction
What the sources say
- Partly supportedcites [43] von Luxburg, Ulrike, 2008, Consistency of spectral clustering, Ann. Statist. sourceExperimental — accuracy not yet measured
The Introduction describes clustering as widely used and says its consistency guarantees are unsatisfactory. The discussion following Proposition 24 shows that unnormalized spectral clustering can fail and that its reliability can be difficult to assess. These passages support the general concern about reliability, but they do not identify arbitrary threshold choices as a difficulty or establish a lack of robustness for clustering generally; no page numbers are available in our copy. (LLM, conf=0.82)
- Not matchedcites [39] kleinbergJ.M. Kleinberg, An impossibility theorem for clustering, NIPS 2002: 446-453.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“In particular, in [41], indications were found that the data was concentrated around an annulus.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.4. Example: Natural image statistics.
What the sources say
- Supportedcites [41] lee A.B. Lee, K.S. Pedersen, and D. Mumford, The nonlinear statistics of high-contrast patches in natural images, International Journal of Computer Vision (54), No. 1-3, August 2003, pp. 83-103. source
Section 5.1 describes the ideal-edge manifold using a circular orientation parameter and a bounded displacement parameter, giving it an annulus-like parametrization. Section 5.2 finds evidence that optical data points concentrate near that manifold. The paper does not use the word “annulus,” but the citing sentence is a reasonable, hedged paraphrase. (LLM, conf=0.82)
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“By using persistence, one is able to develop a systematic way of addressing such questions.That methodology has been described in [65].”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 4.3. Tree based persistence.
What the sources say
- Supportedcites [65] Zomorodian, Afra, 2008, Localized homology, Comput. Geom. source
Section 1.2 describes a method for localizing topological attributes relative to a cover. Section 4.4 gives the persistence algorithm that computes representatives of homology classes, supplying descriptions of those attributes. Together, these passages support the paraphrase about systematically addressing where features come from. (LLM, conf=0.90)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“The algorithm Ξ is the algorithm which associates to a finite metric space (X, d X ) the persistent set {π 0 (V R(X, ))} ≥0 .”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
What the sources say
- Supportedcites [9] memolicarlsson G. Carlsson and F. Memoli, Persistent Clustering and a Theorem of J. Kleinberg , Preprint, March 2008. source
Example 2.1 (Section 2, p. 3) associates a finite metric space with a persistent set whose blocks at scale r contain exactly the points connected by chains of distances at most r. Those blocks are the connected components of the Vietoris–Rips complex at that scale, as the claim describes. (LLM, conf=0.90)
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Also, results about barcodes under the general heading have now begun to appear (see [15] and [16]).”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Additional information
What the sources say
- Supportedcites [15] Cohen-Steiner, David, 2007, Stability of persistence diagrams, Discrete Comput. Geom. source
The Introduction (p. 2) says the paper makes concrete statements about the stability of barcodes. The Applications section (p. 16) states a Barcode Theorem, making this paper an example of the barcode results the citing sentence says have begun to appear. (LLM, conf=0.90)
- Not matchedcites [16] stabilitytwo D. Cohen-Steiner, H. Edelsbrunner, J. Harer and Y. Mileyko, Lipschitz functions have 𝐿_{𝑝}-stable persistence, Found. Comput. Math., to appear.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Supported
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“It is readily checked that θ is a functor from N pers (Ab) to the category of graded Z[t]-modules, and is in fact an equivalence of categories, since an inverse functor can be given by M * → {M n }, where the morphisms ψ mn are given by multiplication by t n-m .”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1=2
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A first observation is that the points are scattered throughout the 7-sphere, in the sense that no point on S 7 is very far from the set, but that the density appears to vary a great deal.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.4. Example: Natural image statistics.
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We note that there are a number of short lines, and one long one.According to the philosophy mentioned above, this suggests that the first Betti number should be estimated to be one.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.4. Example: Natural image statistics.
hedgedcorrelationalSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We note that in this case, there are many short segments and 5 longer segments.It is again the case that this barcode is stable over the choices of landmark points and samples from M. This suggests that the first Betti number of the putative underlying space should be five.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Primary circle
hedgedcorrelationalSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Note that while the two smaller holes have now been “filled in”, a new hole has been introduced in the lower right-hand portion of the figure.Consequently, if we were to compute the homology of this complex, we would obtain a first Betti number of two.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1=2
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“We note that the vertical circles intersect the horizontal primary circle in two points, and do not intersect each other, so the picture in question produces a natural embedding of the three circle model in the Klein bottle.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Three circle model in the data
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We note that the β 0 barcode (the upper picture) clearly shows the single component, the β 1 shows two lines from threshhold parameter values .15 to .35, and finally the β 2 barcode shows a single line on roughly the same interval.This gives β 0 = 1, β i = 2, and β 2 = 1.These are the mod 2 Betti numbers for the Klein bottle.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Three circle model in the data
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“At both scales, there is a central dense core, and two”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 3.5. Examples.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These two are distinguished by mod 3 homology, and we have performed the computation to show that the mod 3 homology is consistent with the Klein bottle and not with the torus.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Three circle model in the data
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We note that given only the Mapper output, one might suspect that the small feature (the array of orange nodes) could simply be an artifact, but examination of the data shows that they correspond to essentially distinct contact maps.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 3.5. Examples.
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Some experimentation with the three circle model results in the following picture in which the horizontal segments form the primary circle and the vertical segments form the secondary circles, as in the diagram.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Three circle model in the data
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We have informally confirmed that the indicated patches are the ones which occur in the high density portions of the data set.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Three circle model in the data
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“By far the most frequently occurring signatures were (1, 1, 0) and (1, 0, 1), corresponding to a circle and a sphere, respectively.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.5. Example: Electrode array data from primary visual cortex.
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Note that the two secondary circles each intersect the primary circle in two points, and that they do not intersect each other.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Three circle model in the data
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Note also that the data are obtained by simply examining the states occurring in the simulation, and that it does not include any dynamic information which would show how the states are traversed in the folding process.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 3.5. Examples.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The set S exhibits the three circle model clearly, but enlarging it still does not exhibit the non-trivial β 2 which would be characteristic of the Klein bottle.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Three circle model in the data
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“It is easy to check (a) that the factorization is a homeomorphism and (b) that the orbit space is homeomorphic to a Klein bottle.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Three circle model in the data
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“A M iso -functorial clustering algorithm determines an choice p ι ∈ P G ι ι for every ι, and conversely an arbitrary choice of such p ι 's determines an M iso -functorial clustering algorithm.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Each of these notions creates the structure of a category whose objects are the finite metric spaces, since one can readily observe that each of the classes of morphisms is closed under composition and contains the identity.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Some work in clustering theory has been done in trying to determine the optimal choice of , but it is now well understood that it is much more informative to maintain the entire dendrogram of the set, which provides a summary of the behavior of clustering under all possible values of the parameter at once.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1. Introduction
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In the biological context, notions of distance are constructed using some intuitively attractive measures of similarity (such as BLAST scores or their relatives), but it is far from clear how much significance to attach to the actual distances, particularly at large scales.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1. Introduction
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Topology studies only properties of geometric objects which do not depend on the chosen coordinates, but rather on intrinsic geometric properties of the objects.As such, it is coordinate-free.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1. Introduction
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Moreover, it is understood that most of the information about topological spaces can be obtained through diagrams of discrete sets, via a process of simplicial approximation.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1. Introduction
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We then demonstrate that persistence can be generalized in several different directions, providing more structure and information about the data sets in question.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Example.
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“If the clusterings are consistent, i.e. if the clusters in C(X 1 ) and C(X 2 ) in C(X 1 ∪ X 2 ) correspond well under these maps, then one can regard that as evidence that the subsample clusterings actually correspond to clusterings on the full data set X.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Example.
hedgedcorrelationaln = 2- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“For any topological space X with a finite number of path components, β 0 (X) is the number of path components.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Example.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“We begin with a discussion of persistent homology, which is a mathematical formalism which permits us to infer topological information from a sample of a geometric object, and we show how it can be applied to particular data sets arising from natural image statistics and neuroscience.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Example.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Functoriality is central in algebraic topology in that the functoriality of homological invariants is what permits one to compute them from local information, and that functoriality is at the heart of most of the interesting applications within mathematics.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1. Introduction
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We then show that the philosophy of functoriality can be used to reason about the nature of clustering methods, and we conclude by speculating about theorems one might hope to prove and discussing how the subject might develop more generally.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Example.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“One important goal of data analysis is to allow the user to obtain knowledge about the data, i.e. to understand how it is organized on a large scale.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1. Introduction
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Although we often receive data in the form of vectors of real numbers, it is frequently the case that the coordinates, like the metrics mentioned above, are not natural in any sense, and that therefore we should not restrict ourselves to studying properties of the data which depend on any particular choice of coordinates.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1. Introduction
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The second method is more efficient, since it is precisely adapted to the complex at hand.Furthermore, it contains complete information about the original R-vector space.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1=2
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In interpreting the output, one now finds that long intervals in the output barcode indicate the presence of a homology class which “persists” over a long range of parameter values, while short intervals indicate cycles which are “born” at a given parameter value and then “die” at a nearby parameter value.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1=2
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“This is due to the fact that for finite metric spaces, it is generically the case that each value of the distance is taken only for one pair of points, so one does not have any points which are equidistant between a pair of landmarks.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.2. Building coverings and complexes.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Intuitively, we regard these two holes as coming from faulty sampling or other errors in the recovery of the data.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1=3
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The pictures and discussion above allow us to formulate the intuition that long intervals correspond to large scale geometric features in the space and that short intervals correspond to noise or inadequate sampling.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1=2
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Both the the Čech and Vietoris-Rips complexes typically produce simplices in dimensions much higher than the dimension of the space.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.2. Building coverings and complexes.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“It turns out that H simp k (X, Z) is always canonically isomorphic to the singular homology of the space |X|.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Remark.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The conclusion is that for simplicial complexes, homology is algorithmically computable, although standard methods for computing Smith normal form can often be painfully slow.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Remark.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“If further is sufficiently small, then all the balls will be geodesically convex, and the complex will compute the homology of M correctly.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.3. Persistent homology.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Note that if the centers actually lie on a submanifold M ⊆ R n , and the set X is sufficiently dense in M , then this complex is the Čech complex attached to a covering of M .”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.3. Persistent homology.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“It is clearly the case that if we have 0 ≤ ≤ , then we have an inclusion W s (X, L, ) ↩→ W s (X, L, ) and similarly for W s V R , W w , and W w V R .”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.2. Building coverings and complexes.
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, for finite metric spaces, it generically produces degenerate (i.e.discrete) complexes, with no one dimensional simplices.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.2. Building coverings and complexes.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In rough terms, δ k for large values of k computes density using points in large neighborhoods of x, and for small values uses small neighborhoods.So, δ k for large k corresponds to a smoothed out notion of density, and for small k corresponds to a version which carries more of the detailed structure of the data set.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.4. Example: Natural image statistics.
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“It was observed in [38] that in an informal sense, the images in the different conditions appeared to be quite similar, and a statistical analysis strongly suggested that the behavior of V1 in the spontaneous condition was consistent with a behavior which consisted in moving through a family of evoked images corresponding to responses to angular boundaries, without any particular order.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.5. Example: Electrode array data from primary visual cortex.
What the sources say
- Not checked — no full text of the sourcecites [38] kenet T. Kenet, D. Bibitchkov, M. Tsodyks, A. Grinvald, and A. Arieli, Spontaneously emerging cortical representations of visual attributes, Nature 425 (2003), 954-956. source
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“This result is consistent with what was obtained by using ISOMAP in [61].”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 3.5. Examples.
What the sources say
- Not checked — no full text of the sourcecites [61] isomapJ.B. Tenenbaum, V. de Silva and J.C. Langford, A global geometric framework for nonlinear dimensionality reduction, Science 290 (2000) (December), pp. 2319-2323. source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The arrays of neurons studied in [59] are from the primary visual cortex or V1 in Macaque monkeys.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.5. Example: Electrode array data from primary visual cortex.
What the sources say
- Not checked — no full text of the sourcecites [59] visionG. Singh, F. Memoli, T. Ishkhanov, G. Carlsson, G. Sapiro and D. Ringach, Topological Structure of Population Activity in Primary Visual Cortex, Journal of Vision, Volume 8, Number 8, Article 11, pp. 1-18, 2008. source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“since the intuition is that features which appear at several levels in such a multiresolution diagram would be more intrinsic to the data set than those which appear at a single level.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Definition 3.2.
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The core consists of normal or near-normal patients, and the two flares consist of patients with the two different forms of diabetes.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 3.5. Examples.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Z[m, n]-persistence vector spaces are examples of quiver representations, a highly developed area of algebra.See [29] for a complete description.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Corollary 4.6.
What the sources say
- Not checked — no full text of the sourcecites [29] Gabriel, P., 1997 source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These functions are clearly invariants of the isomorphism class of the module M , and we refer to them as the dimension and rank invariants, respectively.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Definition 4.3.
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“If the question is instead whether or not an element arises from a union of k elements of S, then we do not obtain complete information this way, but we do obtain partial information in that we can preclude the possibility that a class arises from such a union.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 4.3. Tree based persistence.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“We note that the correspondence X → π 0 (X) can actually be viewed as a functor (see [44]) from the category of topological spaces to the category of sets, in the sense that a continuous map f : X → Y induces a map of sets π 0 (f ) : π 0 (X) → π 0 (Y )”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
What the sources say
- Not matchedcites [44] Mac Lane, Saunders, 1998
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As we have seen above, algebraic topology is capable of producing signatures which indicate the presence of topological features within a space.As it stands, however, it is not capable of describing the source of the feature, i.e.where in the space the hole or other feature is located.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 4.3. Tree based persistence.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Theorem 4.5.Let M denote any Z[m, n]-persistent F -vector space, so that every vector space V i is finite dimensional.Then there is an isomorphism”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Example.
What the sources say
- Not checked — no full text of the sourcecites [29] Gabriel, P., 1997 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“As Kleinberg points out, it is possible to do so in a number of different ways by specifying cost functions on particular clusterings, and prove a uniqueness result for optimal choices of clusterings with respect to this cost function”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
What the sources say
- Not matchedcites [39] kleinbergJ.M. Kleinberg, An impossibility theorem for clustering, NIPS 2002: 446-453.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“This machinery is not available in the multivariable case, but it turns out that it can be replaced by the Gröbner basis methodology (see [18] or [47]).”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Definition 4.3.
What the sources say
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Any finite Z-persistence F -vector space M can be decomposed as”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Corollary 4.6.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The properties of trees guarantee that ≤ T is a partial ordering.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 4.3. Tree based persistence.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
In the paper
“In the paper [59], this was shown by simulation, which is of course always an option.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
What the sources say
- Not checked — no full text of the sourcecites [59] visionG. Singh, F. Memoli, T. Ishkhanov, G. Carlsson, G. Sapiro and D. Ringach, Topological Structure of Population Activity in Primary Visual Cortex, Journal of Vision, Volume 8, Number 8, Article 11, pp. 1-18, 2008. source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Not only is the correspondence π 0 (-) a functor from spaces to sets, it is equipped with a natural surjective map of sets η X : X → π 0 (X).”
- Also stated in: § 5. Reasoning about clustering · plain
“Further, these surjective maps have the property that for every continuous map f : X → Y , the diagram of sets (below) commutes.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
- Also stated in: § 5. Reasoning about clustering · plain
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“While the observation is of course suggestive, to prove that it indicates structure in the data set one must show that segments of the indicated length cannot occur in a random model associated to a null hypothesis.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This means that Kleinberg's conditions also make sense in this context.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
In the paper
“We point out that in the context of ordinary homology (i.e.not persistent homology), M. Penrose and others have developed a theory of “geometric random graphs” and have proved various results concerning the Betti numbers of complexes attached to such graphs (see [54]).”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Additional information
What the sources say
- Not checked — no full text of the sourcecites [54] Penrose, Mathew, 2003 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It is clear that a C-functorial clustering algorithm is a clustering algorithm in the sense of Kleinberg, since the surjective map of sets from X to χ(X, d X ) yields a partition of X, namely the partition whose blocks are the sets η -1 X (z), as z ranges over all elements of χ(X, d X ).”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
None of the sources cited for this claim have been checked yet.
In the paper
“Each such interval can be considered as a point in the set D = {(x, y) ∈ R 2 |x ≤ y}, and the output is therefore a finite subset of the set D. As such, we now have a finite spatial point process (see [20], [3]).”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Additional information
What the sources say
- Not matchedcites [20] Daley, D. J., 1988
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked — no full text of the sourcecites [3] Baddeley, Adrian, 2007, Spatial point processes and their applications source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
The paper relies on this without support.
There is no classification theorem for R-persistence abelian groups.
“However, we do not have such a theorem.”
The paper relies on this without support.
There is still no classification theorem for graded Z[t]-modules.
“Now, there is still no classification theorem for graded Z[t]-modules.”
The paper relies on this without support.
In studying data from experiments, one typically cannot assume that the data lies along a submanifold.
“If one is interested in studying data from experiments, though, one typically cannot assume that the data lies along a submanifold.”
The paper relies on this without support.
Even if one could assume that the data lies along a manifold, one is usually not in a position to verify that the stringent hypotheses of [52] are satisfied.
“Further, even if one could assume that the data lies along a manifold, one is usually not in a position to verify that the stringent hypotheses of [52] are satisfied.”
The paper relies on this without support.
The analysis of populations of neurons is amenable to geometric analysis.
“The second problem appears to be very amenable to geometric analysis, since it will involve the activities of several neurons at once.”
The paper relies on this without support.
The mechanism by which V1 carries out low-level tasks such as edge and line detection is not understood.
“However, the mechanism by which it carries out these tasks is not understood.”
The paper relies on this without support.
The classification of finitely generated modules over multivariable polynomial rings is much more complicated than the corresponding result for single-variable polynomial rings, and no reasonable parametrization is known.
“As is well known to algebraists, the classification of finitely generated modules over multivariable polynomial rings is much more complicated than the corresponding result for single variable polynomial rings, and in fact no reasonable parametrization is known.”
The paper relies on this without support.
In the case of more than one variable, the classification of multigraded modules depends on the field in question.
“examples show that in the case of more than one variable, the classification of multigraded modules definitely depends on the field in question.”
The paper relies on this without support.
The classification in the multivariable case is parametrized by points in moduli varieties over the ground field.
“In fact, the classification in the multivariable case is parametrized by points in moduli varieties over the ground field”
The paper relies on this without support.
Hierarchical clustering algorithms do not report single partitions but rather dendrograms, which are roughly speaking R+-persistent sets.
“This is not an unreasonable thing to do in view of the fact that hierarchical clustering algorithms do not report single partitions but rather dendrograms, which are roughly speaking R + -persistent sets.”
Citations
Cites
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
Bootstrap methods: another look at the jackknife
Efron, B. (1979) · The Annals of Statistics · doi:10.1214/aos/1176344552 · Cited by 17,602
Density Estimation for Statistics and Data Analysis
Silverman, B. W. (2018) · Routledge eBooks · doi:10.1201/9781315140919 · Cited by 15,938
Nonlinear Dimensionality Reduction by Locally Linear Embedding
Roweis, S. T., & Saul, L. K. (2000) · Science · doi:10.1126/science.290.5500.2323 · Cited by 14,965
A Global Geometric Framework for Nonlinear Dimensionality Reduction
Tenenbaum, J. B., Silva, V. D., & Langford, J. C. (2000) · Science · doi:10.1126/science.290.5500.2319 · Cited by 13,585
Categories for the Working Mathematician
Lane, S. M. (1971) · Graduate texts in mathematics · doi:10.1007/978-1-4612-9839-7 · Cited by 8,982 · cited 3×
“We note that the correspondence X → π0(X) can actually be viewed as a functor (see [44]) from the category of topological spaces to the category of sets, in the sense that a continuous map f : X → Y induces a map of sets π0(f) : π0(X)→ π0(Y ), satisfying certain obvious conditions on composite maps and identity maps.”
BackgroundMethodDensity Estimation for Statistics and Data Analysis
(1986) · Springer eBooks · doi:10.1007/978-1-4899-3324-9 · Cited by 8,569
(1975) · CERN Document Server (European Organization for Nuclear Research) · Cited by 5,958
Density Estimation for Statistics and Data Analysis
Dehnad, K., & Silverman, B. W. (1987) · Technometrics · doi:10.2307/1269475 · Cited by 5,479
(2001) · Cited by 5,374
Research works printed but not linked: 4
- tigran G. Carlsson and T. Ishkanov, Local structure of spaces of natural images, preprint, (2007), available at http://comptop.stanford.edu/preprints/
- Dummit, David S., 2004, Abstract algebra
- Huber, Peter J., 1985, Projection pursuit, Ann. Statist., 13, 435, 10.1214/aos/1176349519 · DOI · Paper page
- Reeb, Georges, 1946, Sur les points singuliers d’une forme de Pfaff complètement intégrable ou d’une fonction numérique, C. R. Acad. Sci. Paris, 222, 847
Cited by
Sorted by citations
Items 101–200 of 2,331
Prediction and mitigation of mutation threats to COVID-19 vaccines and antibody therapies
Chen, J., Gao, K., Wang, R., & Wei, G. (2021) · Chemical Science · doi:10.1039/d1sc01203g · Cited by 110
Sheaves, Cosheaves and ApplicationsPreprint
Curry, J. M. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1303.3255 · Cited by 205
The ring of algebraic functions on persistence bar codes
Adcock, A., Carlsson, E., & Carlsson, G. (2016) · Homology Homotopy and Applications · doi:10.4310/hha.2016.v18.n1.a21 · Cited by 124
“The reason is that under the parametrization of persistence vector spaces as described in [3] and [13], intervals of length zero correspond to zero vector spaces, and therefore all intervals of length zero should be considered equal.”
BackgroundSelf-citationMechanisms of SARS-CoV-2 Evolution Revealing Vaccine-Resistant Mutations in Europe and America
Wang, R., Chen, J., & Wei, G. (2021) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.1c03380 · Cited by 110
Evolution of network architecture in a granular material under compression
Papadopoulos, L., Puckett, J. G., Daniels, K. E., & Bassett, D. S. (2016) · Physical review. E · doi:10.1103/physreve.94.032908 · Cited by 103
Patania, A., Vaccarino, F., & Petri, G. (2017) · EPJ Data Science · doi:10.1140/epjds/s13688-017-0104-x · Cited by 106
Computing Topological Persistence for Simplicial Maps
Dey, T. K., Fan, F., & Wang, Y. (2014) · Annual Symposium on Computational Geometry (SoCG) · doi:10.1145/2582112.2582165 · Cited by 118
“While the idea of maintaining a cohomology basis through an inclusion is not new, maintaining them through a vertex collapse is new, which constitutes an important atomic operation for simulating simplicial maps.”
BackgroundLearning spatial relationships between objects
Rosman, B., & Ramamoorthy, S. (2011) · The International Journal of Robotics Research · doi:10.1177/0278364911408155 · Cited by 103
“This view of what constitutes an object is inspired by emerging notions regarding topological invariance and qualitative structure in a wide variety of data sets, as in work by Carlsson (2009), and illustrates the level of description we are aiming towards with our representation scheme.”
BackgroundMethodPersistent Homology for Path Planning in Uncertain Environments
Bhattacharya, S., Ghrist, R., & Kumar, V. (2015) · IEEE Transactions on Robotics · doi:10.1109/tro.2015.2412051 · Cited by 105
“See [8], [12] for details.”
BackgroundInfluentialNielson, J. L., Cooper, S. R., Yue, J. K., Sorani, M. D., Inoue, T., Yuh, E. L., Mukherjee, P., Petrossian, T. C., Paquette, J., Lum, P. Y., et al. (2017) · PLoS ONE · doi:10.1371/journal.pone.0169490 · Cited by 100
“TDA is mathematically well-suited for dealing with this complexity (see below)[8,9,11].”
BackgroundSelf-citationPersistent homology of complex networks for dynamic state detection
Myers, A., Munch, E., & Khasawneh, F. A. (2019) · Physical review. E · doi:10.1103/physreve.100.022314 · Cited by 105
Robust Topological Inference: Distance To a Measure and Kernel DistancePreprint
Chazal, F., Fasy, B. T., Lecci, F., Michel, B., Rinaldo, A., & Wasserman, L. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1412.7197 · Cited by 120
“Now we summarize some important properties of the DTM, all of which are proved in Chazal et al. (2011) and Buchet et al.”
BackgroundInfluentialPersistent Homology of Collaboration Networks
Carstens, C. J., & Horadam, K. J. (2013) · Mathematical Problems in Engineering · doi:10.1155/2013/815035 · Cited by 101
“For a more elaborate introduction to persistent homology we refer to [7, 8].”
BackgroundInfluentialKnowledge gaps in the early growth of semantic feature networks
Sizemore, A. E., Karuza, E. A., Giusti, C., & Bassett, D. S. (2018) · Nature Human Behaviour · doi:10.1038/s41562-018-0422-4 · Cited by 98
Colombo, G., Cubero, R. J. A., Kanari, L., Venturino, A., Schulz, R., Scolamiero, M., Agerberg, J., Mathys, H., Tsai, L.-H., Chachólski, W., et al. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01167-6 · Cited by 100
Hyperspectral Anomaly Detection Based on Chessboard Topology
Gao, L., Sun, X., Sun, X., Zhuang, L., Du, Q., & Zhang, B. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3249748 · Cited by 101
“As a powerful mathematical tool, point set topology can solve multiple types of data mining tasks in ML by disassembling chaotic and disordered data into neat and ordered forms [54], [55].”
Introduction to the R package TDAPreprint
Fasy, B. T., Kim, J., Lecci, F., & Maria, C. (2014) · arXiv (Cornell University) · doi:10.4230/lipics.socg.2020.19 · Cited by 125
Deep Learning with Topological SignaturesPreprint
Hofer, C. D., Kwitt, R., Niethammer, M., & Uhl, A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.04041 · Cited by 123
“Several works have demonstrated that TDA can be beneficial in a diverse set of problems, such as studying the manifold of natural image patches [7], analyzing activity patterns of the visual cortex [26], classification of 3D surface meshes [25, 20], clustering [10], or recognition of 2D object shapes [27].”
BackgroundInfluentialClassification of hepatic lesions using the matching metric
Adcock, A., Rubin, D. L., & Carlsson, G. (2014) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2013.10.014 · Cited by 98
“Persistent homology [3, 18] is an approach to extending the notion of shape to point clouds or finite metric spaces, which has been developed over the last 10-15 years.”
BackgroundSelf-citationCategorification of Persistent Homology
Bubenik, P., & Scott, J. A. (2014) · Discrete & Computational Geometry · doi:10.1007/s00454-014-9573-x · Cited by 139
“For surveys and books on this subject, see [3, 11, 12, 15, 20].”
BackgroundPrincipal Graph and Structure Learning Based on Reversed Graph Embedding
Mao, Q., Wang, L., Tsang, I. W.-H., & Sun, Y. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2635657 · Cited by 92
“Topological data analysis [20] is another related direction to extract simple graph representation of high dimensional datasets.”
Hoffman, D. L., & Novak, T. P. (2015) · SSRN Electronic Journal · doi:10.2139/ssrn.2648786 · Cited by 96
A topological approach for protein classification
Cang, Z., Mu, L., Wu, K., Opron, K., Xia, K., & Wei, G.-W. (2015) · Computational and Mathematical Biophysics · doi:10.1515/mlbmb-2015-0009 · Cited by 93
“Computational homology and persistent homology have been applied to a variety of domains, including image analysis [7, 17, 53, 84, 93], chaotic dynamics verification [64, 77], sensor network [92], complex network [60, 69], data analysis [14, 73, 80, 89, 100], shape recognition [1, 41] , and computational biology [36,…”
BackgroundKepler Mapper: A flexible Python implementation of the Mapper algorithm.
van Veen, H. J., Saul, N., Eargle, D., & Mangham, S. (2019) · The Journal of Open Source Software · doi:10.21105/joss.01315 · Cited by 91
“By computing topological descriptors of data, such as connected components, loops, and voids, we are better able to find hidden relationships among noisy and high-dimensionality data (Carlsson, 2009; Edelsbrunner & Harer, 2010).”
BackgroundRevealing hidden medium-range order in amorphous materials using topological data analysis
Sørensen, S. S., Biscio, C. A. N., Bauchy, M., Fajstrup, L., & Smedskjær, M. M. (2020) · Science Advances · doi:10.1126/sciadv.abc2320 · Cited by 102
“G. Carlsson, Topology and data.”
BackgroundInfluentialLin, J., Liu, Z., Guo, Y., Wang, S., Zhang, T., Xue, X., Li, R., Feng, S., Wang, L., Liu, J., et al. (2023) · Nano Today · doi:10.1016/j.nantod.2023.101802 · Cited by 101
Topological data analysis of single-trial electroencephalographic signals
Wang, Y., Ombao, H., & Chung, M. K. (2018) · The Annals of Applied Statistics · doi:10.1214/17-aoas1119 · Cited by 91
“A promising exploratory approach is topological data analysis (TDA), an umbrella term for various topological techniques to analyze scientific data [Carlsson (2009)].”
BackgroundCompeting spreading dynamics in simplicial complex
Wen-yao, L., Xue, X., Pan, L., Lin, T., & Wang, W. (2021) · Applied Mathematics and Computation · doi:10.1016/j.amc.2021.126595 · Cited by 97
Persistent homology for random fields and complexes
Adler, R. J., Bobrowski, O., Borman, M. S., Subag, E., & Weinberger, S. (2010) · Institute of Mathematical Statistics Collections · doi:10.1214/10-imscoll609 · Cited by 91
“Recent excellent and quite different reviews by Carlsson [9, 12] Edelsbrunner [19] and Ghrist [1, 16, 17, 22] give a broad exposition of the basics of persistent homology.”
BackgroundPersistent-Homology-Based Machine Learning and Its Applications -- A SurveyPreprint
Pun, C. S., Xia, K., & Lee, S. X. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3275996 · Cited by 89
“Based on PB/PD results, different functions are proposed to represent or analyze the topological information (Carlsson, 2009; Bubenik, 2015; Chintakunta et al., 2015).”
BackgroundMethodInfluentialThe Vietoris–Rips complexes of a circle
Adamaszek, M., & Adams, H. (2017) · Pacific Journal of Mathematics · doi:10.2140/pjm.2017.290.1 · Cited by 88
“eatures which persist as being representative of the shape of M. For example, with persistent homology one attempts to reconstruct the homology groups of Mfrom the homology of VR <(X;r) as rvaries [8, 5, 6]. The motivation for using Vietoris{Rips complexes in applied contexts comes from the work of Hausmann and Lat…”
MethodHodgeRank on Random Graphs for Subjective Video Quality Assessment
Xu, Q., Huang, Q., Jiang, T., et al. (2012) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2012.2190924 · Cited by 88
“For more details of persistent homology, readers may refer to the surveys in [37], [38].”
BackgroundDiscrete Morse Theoretic Algorithms for Computing Homology of Complexes and Maps
Harker, S., Mischaikow, K., Mrożek, M., et al. (2013) · Foundations of Computational Mathematics · doi:10.1007/s10208-013-9145-0 · Cited by 88
“∗Department of Mathematics, Rutgers University †Department of Mathematics and BioMaPS Institute, Rutgers University ‡Division of Computational Mathematics, Faculty of Mathematics and Computer Science, Jagiellonian University §Department of Mathematics, University of Pennsylvania (1)See [5] for a description of a compl…”
BackgroundPersistent homology: an introduction and a new text representation for natural language processing
Zhu, X. (2013) · Cited by 93
Topological phase transitions in functional brain networks
Santos, F. A. N., Raposo, E. P., Coutinho-Filho, M. D., Copelli, M., Stam, C. J., & Douw, L. (2019) · Physical review. E · doi:10.1103/physreve.100.032414 · Cited by 85
Topological data analysis of zebrafish patterns
McGuirl, M. R., Volkening, A., & Sandstede, B. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1917763117 · Cited by 90
“ig. 1). As a side note, dimension 0 persistent homology is analogous to single-linkage clustering, so there is a natural connection between TDA- and clustering-based methods for pattern quantification [44]. Using clustering and topological methods in tandem yields both multi-dimensional, coordinate-free summaries (from…”
BackgroundLinear-Size Approximations to the Vietoris–Rips Filtration
Sheehy, D. R. (2013) · Discrete & Computational Geometry · doi:10.1007/s00454-013-9513-1 · Cited by 102
Multidimensional persistence in biomolecular data
Xia, K., & Wei, G. (2015) · Journal of Computational Chemistry · doi:10.1002/jcc.23953 · Cited by 74
Functional summaries of persistence diagrams
Berry, E., Chen, Y., Cisewski-Kehe, J., & Fasy, B. T. (2020) · Journal of Applied and Computational Topology · doi:10.1007/s41468-020-00048-w · Cited by 87
“Persistent homology has drawn interest in applied mathematics (Edelsbrunner et al. 2002, Scopiagno & Zorin 2004, Zomorodian 2005, Ghrist 2008, Carlsson 2009), computer science and machine learning (Adams et al. 2017), statistics (Worsley 1996, Adler et al. 2010, Chazal et al. 2014, Fasy et al.…”
BackgroundMethodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2
Gao, K., Wang, R., Chen, J., Cheng, L., Frishcosy, J., Huzumi, Y., Qiu, Y., Schluckbier, T., Wei, X., & Wei, G. (2022) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00965 · Cited by 93
Encoding the atomic structure for machine learning in materials science
Li, S., Liu, Y., Chen, D., Jiang, Y., Nie, Z., & Pan, F. (2021) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1558 · Cited by 91
“125 Given that atoms in a solid can always be described as a 3D point cloud in spatial coordinates, it is feasible to use persistent homology for the construction of structural features.”
BackgroundPersistent homology analysis of phase transitions
Donato, I., Gori, M., Pettini, M., Petri, G., de Nigris, S., Franzosi, R., & Vaccarino, F. (2016) · Physical review. E · doi:10.1103/physreve.93.052138 · Cited by 81
“Persistent homology [6–8], a particular sampling-based technique from algebraic topology, was originally introduced in 2002 [9] by Edelsbrunner et al. with the aim of extracting coarse topological information from high-dimensional data sets [5].”
Robust and Scalable Learning of Complex Intrinsic Dataset Geometry via ElPiGraph
Albergante, L., Mirkes, E. M., Bac, J., Chen, H., Martin, A., Faure, L., Barillot, E., Pinello, L., Gorban, A. N., & Zinovyev, A. Y. (2020) · Entropy · doi:10.3390/e22030296 · Cited by 83
“Topological data analysis (TDA) focuses on extracting persistent homologies of simplicial complexes derived from a datapoint cloud and also exploits data space geometry [3].”
BackgroundPattern detection in colloidal assembly: A mosaic of analysis techniques
Lotito, V., & Zambelli, T. (2020) · Advances in Colloid and Interface Science · doi:10.1016/j.cis.2020.102252 · Cited by 83
Sarikonda, G., Pettus, J., Phatak, S., Sachithanantham, S., Miller, J. F., Wesley, J. D., Cadag, E., Chae, J., Ganesan, L., Mallios, R., et al. (2014) · Journal of Autoimmunity · doi:10.1016/j.jaut.2013.12.003 · Cited by 84
Analysis of Kolmogorov flow and Rayleigh–Bénard convection using persistent homology
Kramár, M., Levanger, R., Tithof, J., et al. (2016) · Physica D Nonlinear Phenomena · doi:10.1016/j.physd.2016.02.003 · Cited by 83
“We extend prior work by using a relatively new tool called persistent homology [7, 8, 9].”
MethodOzaki, Y., Tierney, A. T., Pfordresher, P. Q., McBride, J. M., Benetos, E., Proutskova, P., Chiba, G., Liu, F., Jacoby, N., Purdy, S. C., et al. (2024) · Science Advances · doi:10.1126/sciadv.adm9797 · Cited by 107
Offroy, M., & Duponchel, L. (2016) · Analytica Chimica Acta · doi:10.1016/j.aca.2015.12.037 · Cited by 80
Topological data analysis: Concepts, computation, and applications in chemical engineering
Smith, A. D. S., Dłotko, P., & Zavala, V. M. (2020) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2020.107202 · Cited by 81
“This perspective brings a number of advantages over other data analysis techniques [13, 67]: • Topology studies data in a manner that is independent of the chosen coordinates.”
BackgroundMethodInfluentialNguyen, D. D., Gao, K., Chen, J., Wang, R., & Wei, G. (2020) · Chemical Science · doi:10.1039/d0sc04641h · Cited by 82
Limit theorems for persistence diagrams
Hiraoka, Y., Shirai, T., & Trinh, K. D. (2018) · The Annals of Applied Probability · doi:10.1214/17-aap1371 · Cited by 81
“at ltration. The persistent homology [9,34] is a tool invented for this purpose, and especially, its expression called persistence diagram is now applied to a wide variety of applied areas (see e.g., [3,10,13,24,31]). From this point of view, there have been some works on a functional of persitence diagram, called lif…”
BackgroundCho, J., & Yoon, Y. (2018) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2018.05.001 · Cited by 80
“In the future, we plan to apply various topological analysis techniques, including topological data analysis (Carlsson, 2009) and medial axis analysis (Jain, 1989; Maurer et al., 2003) to model such continuity of usable airspace.”
MethodDecoding Asymptomatic COVID-19 Infection and Transmission
Wang, R., Chen, J., Hozumi, Y., Yin, C., & Wei, G. (2020) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.0c02765 · Cited by 78
“TML-MP/) which is 12 briefly reviewed as following and its detail can be found in the literature [6]. TML-MP applies element specific persistent homology, which reveals essential biological information [8,14]. The method employs the element-specific persistent homology [7] and other biological and chemical properties as…”
MethodRandom geometric complexes in the thermodynamic regime
Yogeshwaran, D., Subag, E., & Adler, R. J. (2015) · Probability Theory and Related Fields · doi:10.1007/s00440-015-0678-9 · Cited by 80
“[6,9,12,15,45]), all of which share a common theme of studying topological invariants of simplicial complexes built on point sets.”
BackgroundLearning from data with structured missingness
Mitra, R., McGough, S. F., Chakraborti, T., Holmes, C. C., Copping, R., Hagenbuch, N., Biedermann, S., Noonan, J., Lehmann, B., Shenvi, A., et al. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-022-00596-z · Cited by 104
“Patient A with a particular disease status may receive an assay comprising 400 genes, while Patient B of the same disease status may receive an assay comprising 150 genes, depending on the treatment or prognosis goals of the ordering physician.”
Machine Learning Enabled Tailor-Made Design of Application-Specific Metal–Organic Frameworks
Zhang, X., Zhang, K., & Lee, Y. (2019) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.9b17867 · Cited by 77
Emerging Vaccine-Breakthrough SARS-CoV-2 Variants
Wang, R., Chen, J., Hozumi, Y., Yin, C., & Wei, G. (2022) · ACS Infectious Diseases · doi:10.1021/acsinfecdis.1c00557 · Cited by 82
A Survey of Vectorization Methods in Topological Data Analysis
Ali, D., Asaad, A., Jiménez, M.-J., Nanda, V., Paluzo-Hidalgo, E., & Soriano-Trigueros, M. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3308391 · Cited by 111
“1) Metric geometry: construct an increasing family { X t } of cell complexes around the input dataset X , where the indexing t is a scale parameter in R ≥ 0 .”
BackgroundPersistent topological features of dynamical systems
Maletić, S., Zhao, Y., & Rajković, M. (2016) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.4949472 · Cited by 79
“…the approaches originating from geometrical and topological perspective are not novelty in phase space reconstruction [2], the recent advancements in the algebraic topology provide new tools for data analysis [1] and characterization of complex systems [3], and most importantly they are…”
BackgroundNavigating features: a topologically informed chart of electromyographic features space
Phinyomark, A., Khushaba, R. N., Ibáñez‐Marcelo, E., Patania, A., Scheme, E., & Petri, G. (2017) · Journal of The Royal Society Interface · doi:10.1098/rsif.2017.0734 · Cited by 77
“Functional groups of electromyographic features Topological data analysis (TDA) is an approach that focuses on extracting and understanding the ‘shape’ of data using techniques from topology (for an introduction and a survey, see [47,48]).”
BackgroundOnline crowdsourcing subjective image quality assessment
Xu, Q., Huang, Q., & Yao, Y. (2012) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2393347.2393400 · Cited by 74
“Motivated by these two observations, [40] adopts persistent homology [14, 42, 6, 13] to check if a given graph instance satisfies the two conditions.”
BackgroundTopological Function Optimization for Continuous Shape Matching
Poulenard, A., Škraba, P., & Ovsjanikov, M. (2018) · Computer Graphics Forum · doi:10.1111/cgf.13487 · Cited by 73
“The key to our approach is the manipulation of persistence diagrams [CZCG05, Car09].”
MethodIsmail, M. S., Noorani, M. S. M., Ismail, M., Razak, F. A., & Alias, M. A. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106422 · Cited by 74
Autonomous Hamiltonian flows, Hofer’s geometry and persistence modules
Polterovich, L., & Shelukhin, E. (2015) · Selecta Mathematica · doi:10.1007/s00029-015-0201-2 · Cited by 74
“8 is based on the theory of oneparametric persistence modules (see [13,28] for a survey).”
BackgroundRobins, V., Saadatfar, M., Delgado‐Friedrichs, O., et al. (2015) · Water Resources Research · doi:10.1002/2015wr017937 · Cited by 75
“It has rapidly become an important tool for studying shape in application areas from digital images [Bendich et al., 2010; Delgado-Friedrichs et al., 2015] to dynamical systems Kurtuldu et al. [2011] to high-dimensional data-mining [Carlsson, 2009].”
MethodHigh-fidelity phenotyping: richness and freedom from bias
Hripcsak, G. M., & Albers, D. J. (2017) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocx110 · Cited by 73
“Topology 66 is the study of properties that are preserved under the continuous transformation of an object – such as stretching but not tearing – in some real or conceptual space.”
BackgroundWeighted persistent homology for biomolecular data analysis
Meng, Z., Anand, D. V., Lu, Y., Wu, J., & Xia, K. (2020) · Scientific Reports · doi:10.1038/s41598-019-55660-3 · Cited by 74
“Further, different PH based functions are proposed for the visualization, representation and modeling of topological information.(15,22,24) The persistent Betti number (PBN) or Betti curve is one of them.”
Classification of weighted networks through mesoscale homological features
Sizemore, A. E., Giusti, C., & Bassett, D. S. (2016) · Journal of Complex Networks · doi:10.1093/comnet/cnw013 · Cited by 74
“For a mathematical treatment and discussion of computational issues, we point the reader to the following useful references [21, 20, 57, 12].”
BackgroundVolume-Optimal Cycle: Tightest Representative Cycle of a Generator in Persistent Homology
Obayashi, I. (2018) · SIAM Journal on Applied Algebra and Geometry · doi:10.1137/17m1159439 · Cited by 72
“Topological Data Analysis (TDA) [1, 2], which clarifies the geometric features of data from the viewpoint of topology, is developed rapidly in this century both in theory and application.”
BackgroundMetrics for comparing neuronal tree shapes based on persistent homology
Li, Y., Wang, D., Ascoli, G. A., Mitra, P. P., & Wang, Y. (2017) · PLoS ONE · doi:10.1371/journal.pone.0182184 · Cited by 73
“Persistent homology [23] is a basic methodology to characterize and summarize shapes and functions, as well as to identify meaningful features and separate them from ”noise” [11, 22].”
Revealing the Threat of Emerging SARS-CoV-2 Mutations to Antibody Therapies
Chen, J., Gao, K., Wang, R., & Wei, G. (2021) · Journal of Molecular Biology · doi:10.1016/j.jmb.2021.167155 · Cited by 72
“Algebraic topology [45,46] has had tremendous success in describing biochemical and biophysical properties”
Inference of Ancestral Recombination Graphs through Topological Data Analysis
Cámara, P. G., Levine, A. J., & Rabadán, R. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005071 · Cited by 71
“Topological data analysis has emerged during the last decade as a branch of applied topology that attempts to infer topological features of spaces (such as the number of loops and holes) from sets of sampled points [38].”
BackgroundAlgebraic stability of zigzag persistence modules
Botnan, M. B., & Lesnick, M. (2018) · Algebraic & Geometric Topology · doi:10.2140/agt.2018.18.3133 · Cited by 90
“2Tj (y) ag; a2R: Since S"( ) a ˆS"( ) bwhenever ab, this indeed gives a filtration. If our data set is instead a point cloud, we often consider a Vietoris-Rips or Cechˇ filtration; see e.g. [10] for details. TU Munchen, Munich, Germany;¨ botnan@ma.tum.de yPrinceton University, Princeton, NJ, USA; mlesnick@prin…”
BackgroundLee, Y., Barthel, S., Dłotko, P., et al. (2018) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.8b00253 · Cited by 71
“19 One remarkably successful application of TDA was the discovery of a new type of breast cancer based on gene expression data of patients.”
Brain-wide visual habituation networks in wild type and fmr1 zebrafish
Legorreta, E. M., Constantin, L., Piber, M., Favre‐Bulle, I. A., Taylor, M. A., Blevins, A. S., Giacomotto, J., Bassett, D. S., Vanwalleghem, G. C., & Scott, E. K. (2022) · Nature Communications · doi:10.1038/s41467-022-28299-4 · Cited by 72
Persistent Laplacian projected Omicron BA.4 and BA.5 to become new dominating variants
Chen, J., Qiu, Y., Wang, R., & Wei, G. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106262 · Cited by 70
“Advanced mathematics, particularly, persistent homology [6,15,16,33,57,61], offers tremendous abstraction of PPIs.”
BackgroundGeneralized optimal wavelet decomposing algorithm for big financial data
Sun, E. W., Chen, Y., & Yu, M. (2015) · International Journal of Production Economics · doi:10.1016/j.ijpe.2014.12.033 · Cited by 69
“Chen et al. (2012) highlight five big data applications for business intelligence based on the new generation of data analytics savvy.”
BackgroundPersistent homology and Floer–Novikov theory
Usher, M., & Zhang, J. (2016) · Geometry & Topology · doi:10.2140/gt.2016.20.3333 · Cited by 69
“oints of the sample; the structure of the associated persistence module has been used effectively to make inferences about the topological structure of the set Sin some real-world situations, see e.g. [Ca09]. Under finiteness hypotheses on the modules Vt (for instance finite-type as in [ZC05] or more generally pointwise-…”
MethodMuszyński, G., Kashinath, K., Kurlin, V. A., Wehner, M. F., & Prabhat (2019) · Geoscientific model development · doi:10.5194/gmd-12-613-2019 · Cited by 74
“This subsection describes the 2 stages of the atmospheric river pattern recognition method (see Figure 2) based on topological data analysis (TDA) (Carlsson, 2009, 2014) and machine learning (ML) (Kubat, 2015): • Stage 1: This stage applies techniques from topology 5 and algorithms from TDA to…”
BackgroundMethodInfluentialExploring uses of persistent homology for statistical analysis of landmark-based shape data
Gamble, J., & Heo, G. (2010) · Journal of Multivariate Analysis · doi:10.1016/j.jmva.2010.04.016 · Cited by 68
“More recent surveys [10,12,4] include applications and future directions for the subject.”
BackgroundMaximally persistent cycles in random geometric complexes
Bobrowski, O., Kahle, M., & Škraba, P. (2017) · The Annals of Applied Probability · doi:10.1214/16-aap1232 · Cited by 65
“Commonly (see [16, 33]), the persistence of a cycle is measured by the length of the corresponding bar in the barcode, namely by the difference δ(γ ) := γdeath − γbirth.”
BackgroundTopological data analysis of task-based fMRI data from experiments on schizophrenia
Stolz, B. J., Emerson, T., Nahkuri, S., Porter, M. A., & Harrington, H. A. (2021) · Journal of Physics Complexity · doi:10.1088/2632-072x/abb4c6 · Cited by 70
Robins, V., & Turner, K. (2016) · Physica D Nonlinear Phenomena · doi:10.1016/j.physd.2016.03.007 · Cited by 68
“Persistent homology is an algebraic topological tool developed for data analysis that is an intuitively appealing measure of higher-order structure and encompasses spatial correlations of all orders [3, 4].”
A Topological "Reading" Lesson: Classification of MNIST using TDA
Garin, A., & Tauzin, G. (2019) · IEEE International Conference on Machine Learning and Applications (ICMLA) · doi:10.1109/icmla.2019.00256 · Cited by 66
“Filtration Parameters Dim Vectorization Height Direction: [1, 0] 0 Persistent entropy Radial Center: (6, 13) 0 Persistent entropy Height Direction: [1,−1] 0 Persistent entropy Radial Center: (0, 6) 0 Persistent entropy Density #Neighbors: 6 1 Persistent entropy Height Direction: [1, 1] 0 Persistent entropy Height Dire…”
BackgroundMethodHolzinger, A. (2014) · Cited by 65
Exact topological inference of the resting-state brain networks in twins
Chung, M. K., Lee, H., DiChristofano, A., Ombao, H., & Solo, V. (2019) · Network Neuroscience · doi:10.1162/netn_a_00091 · Cited by 64
“The β0 computation is related to single linkage clustering and dendrogram construction (Carlsson, 2009; Carlsson, De Silva, & Morozov, 2009; Carlsson, Singh, & Zomorodian, 2009b; Chowdhury & Mémoli, 2016; Khalid, Kim, Chung, Ye, & Jeon, 2014).”
Topological deep learning: a review of an emerging paradigm
Zia, A., Khamis, A., Nichols, J. D., Tayab, U. B., Hayder, Z., Rolland, V., Stone, E. A., & Petersson, L. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10710-9 · Cited by 74
“Hence they cannot be directly applied to datasets comprised of PDs, and the multi-set information contained in the PD needs to be represented in some vector format.”
InfluentialClassifying Clustering Schemes
Carlsson, G., & Mémoli, F. (2013) · Foundations of Computational Mathematics · doi:10.1007/s10208-012-9141-9 · Cited by 85
“It is also true that functoriality is necessary to extend the methods of clustering to higher order connectivity information with computational topology of finite datasets [3].”
BackgroundMethodSelf-citationQuantifying force networks in particulate systems
Kramár, M., Goullet, A., Kondic, L., & Mischaikow, K. (2014) · Physica D Nonlinear Phenomena · doi:10.1016/j.physd.2014.05.009 · Cited by 65
“A remarkable fact [20,21] is that given a finite filtration it is possible to choose a consistent set of bases for Hn(FN(f , θk)), k = −1, .”
BackgroundMethodNon-empirical identification of trigger sites in heterogeneous processes using persistent homology
Kimura, M., Obayashi, I., Takeichi, Y., Murao, R., & Hiraoka, Y. (2018) · Scientific Reports · doi:10.1038/s41598-018-21867-z · Cited by 64
Hopf algebras and Markov chains: two examples and a theory
Diaconis, P. W., Pang, C. Y. A., & Ram, A. (2013) · Journal of Algebraic Combinatorics · doi:10.1007/s10801-013-0456-7 · Cited by 64
“In topological statistics [17, 18] one studies things like the Betti numbers of C as a function of .”
BackgroundLi, M., Frank, M. H., Coneva, V., Mio, W., Chitwood, D. H., & Topp, C. N. (2018) · PLANT PHYSIOLOGY · doi:10.1104/pp.18.00104 · Cited by 61
Applications of Topological Data Analysis in Oncology
Bukkuri, A., Andor, N., & Darcy, I. K. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.659037 · Cited by 74
“For more details regarding persistent homology and barcodes (please see Ghrist, 2008; Carlsson, 2009; Edelsbrunner and Harer, 2010; Otter et al., 2017).”
BackgroundTopological Characterization of Complex Systems: Using Persistent Entropy
Merelli, E., Rucco, M., Sloot, P. M. A., & Tesei, L. (2015) · Entropy · doi:10.3390/e17106872 · Cited by 60
“Simplicial complexes are analyzed via persistent homology, which allows one to identify the generators of persistent n -dimensional holes [4,10].”
BackgroundMethodGeometrical and topological approaches to Big Data
Snåšel, V., Nowaková, J., Xhafa, F., & Barolli, L. (2016) · Future Generation Computer Systems · doi:10.1016/j.future.2016.06.005 · Cited by 64
“Geometrical and Topological methods are tools for analysing highly complex data [10].”
BackgroundMethodInfluentialDecoding of Neural Data Using Cohomological Feature Extraction
Rybakken, E., Baas, N. A., & Dunn, B. A. (2018) · Neural Computation · doi:10.1162/neco_a_01150 · Cited by 61
“ersistent homology is often used instead, but we will need cohomology for the decoding step. We refer to Ghrist, 2008; Edelsbrunner and Harer, 2010 for introductions to persistence, and Ghrist, 2014; Carlsson, 2009 for introductions to applied topology in general. Figure 5: Left: A ltration together with its rst persi…”
Topological consistency via kernel estimation
Bobrowski, O., Mukherjee, S., & Taylor, J. (2016) · Bernoulli · doi:10.3150/15-bej744 · Cited by 58
“A statistical perspective of the recent efforts in topological data analysis (TDA) [13, 22] has been to compute topological invariants from observations generated by a random process.”
BackgroundA population background for nonparametric density-based clusteringPreprint
Chacón, J. E. (2014) · Cited by 58
Bergomi, M. G., Frosini, P., Giorgi, D., & Quercioli, N. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0087-3 · Cited by 60
Persistent Homology for Fast Tumor Segmentation in Whole Slide Histology Images
Qaiser, T., Sirinukunwattana, K., Nakane, K., Tsang, Y., Epstein, D., & Rajpoot, N. (2016) · Procedia Computer Science · doi:10.1016/j.procs.2016.07.033 · Cited by 60
Metadata
- Authors
- Carlsson, G.
- Affiliations
- Stanford University (US)
- Corresponding author
- Carlsson, Gunnar
- ORCID
- Carlsson, Gunnar
- Venue
- Bulletin of the American Mathematical Society
- Publisher
- American Mathematical Society (AMS)
- Published
- 2009-01-29
- Volume
- 46
- Issue
- 2
- Pages
- 255-308
- ISSN
- 0002-9904 (ISSN-L)
- 1088-9485
- 0273-0979
- OpenAlex
- W1991566301
- Cited by
- 2,302
- References
- 78
- Citations by year
- 2026: 215 · 2025: 216 · 2024: 158 · 2023: 176 · 2022: 159 · 2021: 215 · 2020: 227 · 2019: 151 · 2018: 163 · 2017: 132 · 2016: 128 · 2015: 95 · 2014: 99 · 2013: 65 · 2012: 46
- FWCI
- 33.43
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Bulletin of the American Mathematical Society (publishedVersion)
- Funders
- National Science Foundation (0354543)
- Defense Advanced Research Projects Agency
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Topological and Geometric Data Analysis
- Physical Sciences › Mathematics › Geometry and Topology › Geometric and Algebraic Topology
- Physical Sciences › Mathematics › Applied Mathematics › Geometric Analysis and Curvature Flows
- Keywords
- point clouds, distance measures, high-dimensional data, biological data, microarray data, high-throughput data
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-11, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-11 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review