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.
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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.
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- 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.
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- 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.
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- 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
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“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
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Cited by
Sorted by citations
Items 501–600 of 2,331
Hardy, N., Moreaud, M., Guillaume, D., et al. (2017) · Journal of Microscopy · doi:10.1111/jmi.12523 · Cited by 22
Revealing neural correlates of behavior without behavioral measurementsPreprint
Rubin, A., Sheintuch, L., Brande-Eilat, N., Pinchasof, O., Rechavi, Y., Geva, N., & Ziv, Y. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/540195 · Cited by 22
Levenson, R. M., Singh, Y., Rieck, B., Hathaway, Q. A., Farrelly, C., Rozenblit, J., Prasanna, P., Erickson, B. J., Choudhary, A., Carlsson, G., et al. (2024) · Laboratory Investigation · doi:10.1016/j.labinv.2024.102060 · Cited by 25
Self-citationFrom data-poor to data-rich: system dynamics in the era of big data
Pruyt, E. (2014) · Data Archiving and Networked Services (DANS) · Cited by 25
Multiplex Financial Networks: Revealing the Level of Interconnectedness in the Banking System
de la Concha, A., Martínez-Jaramillo, S., & Carmona, C. U. (2017) · Studies in computational intelligence · doi:10.1007/978-3-319-72150-7_92 · Cited by 21
“Some of the most important advances in this field have being done by Carlson [7] and Edelsbrunner [9,10].”
BackgroundEmpirical study of financial crises based on topological data analysis
Guo, H., Xia, S., An, Q., Zhang, X., Sun, W., & Zhao, X. (2020) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2020.124956 · Cited by 22
Hajij, M., Zamzmi, G., & Batayneh, F. (2021) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc46164.2021.9629828 · Cited by 21
“On the other hand, TDA [8], [17] has emerged recently as a robust tool to extract and compare the structure of datasets.”
BackgroundTime-Resolved Topological Data Analysis of Market Instabilities
Katz, Y. A., & Biem, A. (2020) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2021.125816 · Cited by 25
Liang, D., Xia, S., Zhang, X., & Zhang, W. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.745671 · Cited by 21
“…an important biological basis for the diagnosis and treatment of ASD from the perspective of FC. Persistent homology, a newly developed method in topological data analysis (Carlsson, 2009), is an effective tool for analyzing high-dimensional nonlinear data and exploring its nonlinear structures.”
MethodYen, P. T.-W., Xia, K., & Cheong, S. A. (2021) · Entropy · doi:10.3390/e23091211 · Cited by 22
“Built on the foundations of topology [143–147], group theory [148,149], linear algebra [150,151], and graph theory [152–154], TDA has since became a popular field in applied mathematics, and has also found many applications in data analytics [58].”
BackgroundA universal null-distribution for topological data analysis
Bobrowski, O., & Škraba, P. (2023) · Scientific Reports · doi:10.1038/s41598-023-37842-2 · Cited by 38
Topological tail dependence: Evidence from forecasting realized volatility
Souto, H. G. (2023) · The Journal of Finance and Data Science · doi:10.1016/j.jfds.2023.100107 · Cited by 21
Data Science: A New Paradigm in the Age of Big-Data Science and Analytics
Concolato, C., & Chen, L. M. (2017) · New Mathematics and Natural Computation · doi:10.1142/s1793005717400038 · Cited by 21
Ray, J. S., Johnny, O., Trovati, M., Sotiriadis, S., & Bessis, N. (2018) · Big Data and Cognitive Computing · doi:10.3390/bdcc2030022 · Cited by 20
“In particular, it utilises filter functions to assess and analyse dataset by applying dimensionality reduction [22], where data points are linked via specifically identified clusters, which provides a topological data summary [31].”
MethodDubuc, B., Ebrahimkhanlou, A., & Salamone, S. (2019) · Smart Materials and Structures · doi:10.1088/1361-665x/ab0e96 · Cited by 21
“Conversely, those which only appear over a brief range of may be treated as noise [26].”
BackgroundInfluentialNonparametric inference via bootstrapping the debiased estimator
Cheng, G., & Chen, Y. (2019) · Electronic Journal of Statistics · doi:10.1214/19-ejs1575 · Cited by 22
“The bootstrap consistency given in Theorem 4 shows that our method may be very useful in topological data analysis (Carlsson, 2009; Edelsbrunner and Morozov, 2012; Wasserman, 2018).”
MethodTowards automatic delineation of landslide source and runout
Bhuyan, K., Rana, K., Öztürk, U., et al. (2024) · Engineering Geology · doi:10.1016/j.enggeo.2024.107866 · Cited by 20
Holzinger, A. (2014) · Lecture notes in computer science · doi:10.1007/978-3-662-43968-5_19 · Cited by 21
“Simplicial complexes provide an easy combinatorial way to define certain topological spaces [69].”
BackgroundGromov–Hausdorff Approximation of Filamentary Structures Using Reeb-Type Graphs
Chazal, F., Huang, R., & Sun, J. Z. (2015) · Discrete & Computational Geometry · doi:10.1007/s00454-015-9674-1 · Cited by 20
“In the past decade, with the development of topological methods in data analysis, new theories such as topological persistence (see, for example, [9,11,22,37]) and new tools such as the Mapper algorithm [34] have given rise to new algorithms to extract and visualize geometric and topological…”
MethodExtracting Knowledge from the Geometric Shape of Social Network Data Using Topological Data Analysis
Almgren, K., Kim, M., & Lee, J. (2017) · Entropy · doi:10.3390/e19070360 · Cited by 20
“Topological data analysis is an approach based on applied mathematics that analyzes data using a set of techniques from topology [7,8].”
BackgroundMethodBradley, P. E., Keller, S., & Weinmann, M. (2018) · Remote Sensing · doi:10.3390/rs10101564 · Cited by 20
“This graph is in fact the 1-skeleton of the Vietoris-Rips complex used in topological data analysis [76,77], and a fast construction of it for general metric spaces has been introduced in [78].”
MethodArchitecture and evolution of semantic networks in mathematics texts
Christianson, N., Blevins, A. S., & Bassett, D. S. (2020) · Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rspa.2019.0741 · Cited by 30
“Motivated by a recent report that language acquisition proceeds through an ordered progression filling knowledge gaps [26], we use persistent homology [27, 28, 29, 30] to track the growth and development of topological cavities in the semantic network.”
BackgroundMethodCapturing dynamics of time-varying data via topology
Xian, L., Adams, H., Topaz, C. M., & Ziegelmeier, L. (2021) · Foundations of Data Science · doi:10.3934/fods.2021033 · Cited by 34
“[3;11], [5;10], [5;9], and let 4 = i<j= 8 (see Figure1). Then rank(V(4) !V(8)) is two since there are two intervals ([2;9] and [3;11]) in the persistence diagram that contain the interval [i;j] = [4;8]. The collection of all natural numbers rank(V(i) !V(j)) for all choices of ijis called the rank invariant. 2.4. Th…”
BackgroundInfluentialTopology-controllable Implicit Surface Reconstruction Based on Persistent Homology
Dong, Z., Chen, J., & Lin, H. (2022) · Computer-Aided Design · doi:10.1016/j.cad.2022.103308 · Cited by 19
A Topological Machine Learning Pipeline for Classification
Conti, F., Moroni, D., & Pascali, M. A. (2022) · Mathematics · doi:10.3390/math10173086 · Cited by 24
“The key difference with algebraic topology is that the ranks of the homology groups are computed at various scales and the features we extract are precisely the evolution of such ranks [6,18].”
BackgroundSingh, Y., Jons, W. A., Eaton, J. E., Vesterhus, M. N., Karlsen, T. H., Bjoerk, I., Abildgaard, A., Jørgensen, K. K., Folseraas, T., Little, D. H. W., et al. (2022) · European Radiology Experimental · doi:10.1186/s41747-022-00312-x · Cited by 22
“…topology include the notion of modeling data as a metric space (a set of points along with a measure of how apart any two points are), the definitions of what a simplex and simplicial complex are, the notion of filtrations, and the method of persistent homology [13–15] (Supplementary file 1).”
BackgroundMethodNudged elastic band in topological data analysis
Atanasov, A., Carlsson, G., & Adams, H. (2015) · Topological Methods in Nonlinear Analysis · doi:10.12775/tmna.2015.013 · Cited by 20
“The purpose of topological data analysis [5] is to provide methods which produce simple combinatorial representations of the data.”
MethodSelf-citationLarge-Scale Label-Free Quantitative Mapping of the Sputum Proteome
Burg, D. W., Schofield, J. P. R., Brandsma, J., Staykova, D. K., Folisi, C., Bansal, A. T., Nicholas, B., Yang, X., Rowe, A., Corfield, J., et al. (2018) · Journal of Proteome Research · doi:10.1021/acs.jproteome.8b00018 · Cited by 20
Inference of Cerebrovascular Topology With Geodesic Minimum Spanning Trees
Moriconi, S., Zuluaga, M. A., Jäger, H. R., et al. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2860239 · Cited by 20
“With the development of standardized metrics for fully-connected networks comparison, along the lines of [18], [43], and [55], future works would account a more specific validation focusing on cyclic structures at different scales, since VTrails can fully capture and embed in the over-connected vascular graph all the…”
BackgroundUnsupervised space–time clustering using persistent homology
Islambekov, U., & Gel, Y. R. (2018) · Environmetrics · doi:10.1002/env.2539 · Cited by 19
“For more details on TDA, the reader may consult survey papers such as [21], [7], [22], [23].”
BackgroundFinding Principal Paths in Data Space
Ferrarotti, M. J., Rocchia, W., & Decherchi, S. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2884792 · Cited by 23
“si (t) = twi+1 + (t − 1)wi ; t ∈ [0, 1] Vi = {x ∈ X|i = arg min j d(x, s j )} d(x, si ) = inf t∈[0,1] ||si (t)− x ||”
BackgroundPersistent homology analysis of brain transcriptome data in autism
Shnier, D., Voineagu, M., & Voineagu, I. (2019) · Journal of The Royal Society Interface · doi:10.1098/rsif.2019.0531 · Cited by 17
“Interface 16:(2)0190531 2 to its ability to study properties that are robust against choice of coordinates, choice of metric and more generally continuous deformations, motivating its use in the analysis of transcriptome data [8].”
BackgroundRepresentations of energy landscapes by sublevelset persistent homology: An example with n -alkanes
Mirth, J., Zhai, Y., Bush, J., Alvarado, E., Jordan, H., Heim, M., Krishnamoorthy, B., Pflaum, M. J., Clark, A. E., Zhang, Y., et al. (2021) · The Journal of Chemical Physics · doi:10.1063/5.0036747 · Cited by 20
A higher-dimensional homologically persistent skeleton
Kališnik, S., Kurlin, V. A., & Lešnik, D. (2018) · Advances in Applied Mathematics · doi:10.1016/j.aam.2018.07.004 · Cited by 18
“For excellent introductions to persistent homology, see [16,3,4,13].”
BackgroundČech closure spaces: A unified framework for discrete and continuous homotopy
Rieser, A. (2021) · Topology and its Applications · doi:10.1016/j.topol.2021.107613 · Cited by 20
Velsko, S. P., & Bates, T. (2016) · Health Security · doi:10.1089/hs.2015.0063 · Cited by 20
Persistent homology analysis of osmolyte molecular aggregation and their hydrogen-bonding networks
Xia, K., Anand, D. V., Saxena, S., & Mu, Y. (2019) · Physical Chemistry Chemical Physics · doi:10.1039/c9cp03009c · Cited by 17
A spectral sequence for parallelized persistence
Lipsky, D., Skraba, P., & Vejdemo-Johansson, M. (2011) · arXiv · doi:10.48550/arxiv.1112.1245 · Cited by 22
“3] is a powerful result from algebraic topology that has seen repeated use in computational topology – all the way from motivating why the commonly used filtered simplicial complex constructions capture the original topology [3] to generating particular new results.”
BackgroundTopological Signal Processing Over Generalized Cell Complexes
Sardellitti, S., & Barbarossa, S. (2024) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2023.3347069 · Cited by 19
“The first class of meth-ods is Topological Data Analysis (TDA) [2], where the data are mapped into a topological space and the information is extracted in terms of properties of the space.”
BackgroundTopological signatures of interstellar magnetic fields – I. Betti numbers and persistence diagrams
Makarenko, I., Shukurov, A., Henderson, R., et al. (2018) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stx3337 · Cited by 19
“More subtle but no less essential features are revealed by topological filtration, which characterizes statistical properties of the extrema of the random field and connectivity of its isosurfaces (Carlsson 2009; Adler et al. 2010; Adler & Taylor 2011; Edelsbrunner 2014).”
BackgroundA study on validating non-linear dimensionality reduction using persistent homology
Paul, R., & Chalup, S. K. (2017) · Pattern Recognition Letters · doi:10.1016/j.patrec.2017.09.032 · Cited by 19
Persistent homology index as a robust quantitative measure of immunohistochemical scoring
Takiyama, A., Teramoto, T., Suzuki, H., Yamashiro, K., & Tanaka, S. (2017) · Scientific Reports · doi:10.1038/s41598-017-14392-y · Cited by 19
Topological Recognition of Critical Transitions in Time Series of Cryptocurrencies
Gidea, M., Goldsmith, D., Katz, Y. A., Roldan, P., & Shmalo, Y. (2018) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2019.123843 · Cited by 86
Deng, R., & Duzhin, F. (2022) · Big Data and Cognitive Computing · doi:10.3390/bdcc6030074 · Cited by 25
“Furthermore, while we did some basic data cleaning, we did not thoroughly experiment with data filtering by a codensity threshold, and this may, according to [2], noticeably improve the performance of TDA.”
BackgroundMethodCOVID-19 severity is associated with population-level gut microbiome variations
Lymberopoulos, E., Gentili, G. I., Budhdeo, S., & Sharma, N. (2022) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2022.963338 · Cited by 18
“Topological Data Analysis (TDA) is an unsupervised machine learning tool based on algebraic topology and differential geometry (Carlsson, 2009).”
Methodla Peña, N. D., & Granados, O. M. (2023) · Information Processing in Agriculture · doi:10.1016/j.inpa.2023.03.001 · Cited by 20
Application of topology-based structure features for machine learning in materials science
Zheng, S., Ding, H., Li, S., Chen, D., & Pan, F. M. (2023) · Chinese Journal of Structural Chemistry · doi:10.1016/j.cjsc.2023.100120 · Cited by 21
Characterizing EEG signals of meditative states using persistent homology and Hodge spectral entropy
Gupta, K. V., Beuria, J., & Behera, L. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105779 · Cited by 20
Ameneyro, B., Maroulas, V., & Siopsis, G. (2024) · Journal of Applied and Computational Topology · doi:10.1007/s41468-023-00160-7 · Cited by 38
“…(Townsend et al. 2020), to the analysis of complex biological networks (Maroulas et al. 2021), and other complex dynamical systems (Perea and Harer 2015; Rouse et al. 2015) and sensor networks (De Silva and Ghrist 2006, 2007; Dlotko et al. 2012; Carlsson and De Silva 2010; Carlsson et al. 2009).”
Uncovering 2-D toroidal representations in grid cell ensemble activity during 1-D behavior
Hermansen, E., Klindt, D. A., & Dunn, B. A. (2024) · Nature Communications · doi:10.1038/s41467-024-49703-1 · Cited by 22
“ogy [8] to study the topology of the population activity.”
Concentration for Poisson U-statistics: Subgraph counts in random geometric graphs
Bachmann, S., & Reitzner, M. (2017) · Stochastic Processes and their Applications · doi:10.1016/j.spa.2017.11.001 · Cited by 17
“Motivations to study these complexes arise particularly from topological data analysis (for a survey on this see [3]), but also from other applications of geometric complexes like sensor networks (see e.”
BackgroundAn eQTL biological data visualization challenge and approaches from the visualization community
Bartlett, C. W., Cheong, S. Y., Hou, L., Paquette, J., Lum, P. Y., Jäger, G., Battke, F., Vehlow, C., Heinrich, J., Nieselt, K., et al. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-s8-s8 · Cited by 19
“Their method is based on visualization of the topology implied by the similarity of different subsets [29].”
MethodPersistent homology of directed networks
Chowdhury, S., & Mémoli, F. (2016) · Asilomar Conference on Signals, Systems and Computers (ACSSC) · doi:10.1109/acssc.2016.7868997 · Cited by 19
“Note that there are no persistent 1-dimensional intervals for X , whereas there is a single 1-dimensional interval, persisting for δ ∈ [3, 4], for Y .”
BackgroundMethodA Novel Approach to Identifying a Neuroimaging Biomarker for Patients With Serious Mental Illness
Madan, A., Fowler, J. C., Patriquin, M. A., Salas, R., Baldwin, P. R., Velasquez, K. M., Viswanath, H., Molfese, D. L., Sharp, C., Allen, J. G., et al. (2017) · Journal of Neuropsychiatry · doi:10.1176/appi.neuropsych.16090174 · Cited by 21
“Details about the six combinations of metric and lens are published elsewhere.(26,27) Strength of association among RSFC brain regions was based on KolmogorovSmirnov (K-S) test statistic with associated p value.”
MethodMining Social Media Data Using Topological Data Analysis
Almgren, K., Kim, M., & Lee, J. (2017) · IEEE International Conference on Information Reuse and Integration (IRI) · doi:10.1109/iri.2017.41 · Cited by 19
“, the study of identifying the connected components of a space, more generally connectivity and homology [2].”
BackgroundMethodSynthesis of Energy-Bounded Planar Caging Grasps Using Persistent Homology
Mahler, J., Pokorny, F. T., Niyaz, S., & Goldberg, K. (2018) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2018.2831724 · Cited by 19
“present paper also builds on recent advances in the topological data analysis [31] and the concept of persistent homology [32]”
BackgroundHuckemann, S., Kim, K., Munk, A., et al. (2016) · MPG.PuRe (Max Planck Society) · Cited by 19
Cycle Registration in Persistent Homology With Applications in Topological Bootstrap
Reani, Y., & Bobrowski, O. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3217443 · Cited by 25
“TOPOLOGICAL Data Analysis (TDA) refers to a set of methods promoting the use of mathematical topology in analyzing data and networks [1], [2], [3].”
BackgroundMethodFinding self-similar behavior in quantum many-body dynamics via persistent homology
Spitz, D., Berges, J., Oberthaler, M. K., & Wienhard, A. (2021) · SciPost Physics · doi:10.21468/scipostphys.11.3.060 · Cited by 19
“Over the past two decades the mathematical field of topological data analysis (TDA) has gained considerable attention, accompanied by far-reaching theoretical and computational developments [1, 2].”
BackgroundLearning quantum phase transitions through topological data analysis
Tirelli, A., & Costa, N. C. (2021) · Physical review. B./Physical review. B · doi:10.1103/physrevb.104.235146 · Cited by 24
Local convex hull support and boundary estimation
Aaron, C., & Bodart, O. (2016) · Journal of Multivariate Analysis · doi:10.1016/j.jmva.2016.01.003 · Cited by 19
“The practical interest of such a property is illustrated in [7, 5].”
BackgroundHuckemann, S., Kim, K.-R., Munk, A., et al. (2016) · Bernoulli · doi:10.3150/15-bej722 · Cited by 19
“In fact, these expand the general scheme of topological persistence introduced by Edelsbrunner et al. [17], cf. also Ghrist [23], Carlsson [8] among many others.”
BackgroundAlgebraic topology for computer vision
Freedman, D. Z., Chen, C., & Freedman, D. Z. (2009) · Cited by 19
Dubut, J., Goubault, É., & Goubault-Larrecq, J. (2015) · Lecture notes in computer science · doi:10.1007/978-3-662-47666-6_14 · Cited by 19
“Just as with persistent homology [2], it is the patterns of change,”
BackgroundApplications of Zigzag Persistence to Topological Data AnalysisPreprint
Tausz, A., & Carlsson, G. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1108.3545 · Cited by 18
“ZAG PERSISTENCE TO TOPOLOGICAL DATA ANALYSIS 9 When using k= 15 one obtains the 3-circle model, where in addition to the primary circle there are two other secondary circles. However, in [CISZ06] and [Car09] these results are obtained by selecting a landmark set and then computing the persistent homology of a lazy-wit…”
BackgroundMethodInfluentialSelf-citationMultidimensional Interleavings and Applications to Topological InferencePreprint
Lesnick, M. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1206.1365 · Cited by 19
“See the reviews [22, 26, 4] and the textbook [23] for treatment of the basics, and Sections 2.”
MethodOptimal rates of convergence for persistence diagrams in Topological Data AnalysisPreprint
Chazal, F., Glisse, M., Labruère, C., & Michel, B. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1305.6239 · Cited by 19
“With the emergence of new geometric inference and algebraic topology tools, computational topology [28] has recently known an important development toward data analysis, giving birth to the field of Topological Data Analysis (TDA) [9] whose aim is to infer multiscale qualitative and quantitative relevant topological s…”
BackgroundInfluentialSimplex2Vec embeddings for community detection in simplicial complexesPreprint
Billings, J., Hu, M., Lerda, G., Medvedev, A. N., Mottes, F., Onicas, A. I., Santoro, A., & Petri, G. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.09068 · Cited by 39
“This intervals can also be mapped in a persistence diagram [41] and distances between persistence diagrams can be used.”
BackgroundMapping firms’ locations in technological space: A topological analysis of patent statistics
Escolar, E. G., Hiraoka, Y., Igami, M., & Ozcan, Y. (2023) · Research Policy · doi:10.1016/j.respol.2023.104821 · Cited by 18
Harnessing the power of topological data analysis to detect change points
Islambekov, U., Yuvaraj, M., & Gel, Y. R. (2019) · Environmetrics · doi:10.1002/env.2612 · Cited by 17
Evolutionary homology on coupled dynamical systems with applications to protein flexibility analysis
Cang, Z., Munch, E., & Wei, G. (2020) · Journal of Applied and Computational Topology · doi:10.1007/s41468-020-00057-9 · Cited by 19
“The interested reader can find further specifics in, e.g., Carlsson (2009), or Edelsbrunner and Harer (2010).”
BackgroundDubuc, B., Sitaropoulos, K., Ebrahimkhanlou, A., et al. (2020) · Structural Health Monitoring · doi:10.1177/1475921720976937 · Cited by 18
Dhakal, K., Zhu, Q., Zhang, B., Li, M., & Li, S. (2021) · Frontiers in Plant Science · doi:10.3389/fpls.2021.614926 · Cited by 18
“To solve this problem, we applied a mathematical approach called persistent homology (Verri et al., 1993; Carlsson, 2009; Edelsbrunner and Harer, 2010) to convert the complex patterns on a 2D image into a topological space where branching patterns are comparable between different samples.”
MethodTopological data analysis of human brain networks through order statistics
Das, S., Anand, D. V., & Chung, M. K. (2023) · PLoS ONE · doi:10.1371/journal.pone.0276419 · Cited by 25
“The connected components characterize the modular structure or shape of a network whereas the cycles are loops in a network [24, 78].”
BackgroundLin, Z., Su, Y., Yang, J., Qiu, C., Chai, X., Liu, X., Ouyang, Q., & Zhang, D. (2023) · Composites Communications · doi:10.1016/j.coco.2023.101688 · Cited by 18
Zigzag Zoology: Rips Zigzags for Homology Inference
Oudot, S., & Sheehy, D. R. (2014) · Foundations of Computational Mathematics · doi:10.1007/s10208-014-9219-7 · Cited by 21
A topological study of repetitive co-activation networks inin vitrocortical assemblies
Pirino, V., Riccomagno, E., Sergio Martinoia, A., & Massobrio, P. (2015) · Physical Biology · doi:10.1088/1478-3975/12/1/016007 · Cited by 18
Rips filtrations for quasimetric spaces and asymmetric functions with stability results
Turner, K. (2019) · Algebraic & Geometric Topology · doi:10.2140/agt.2019.19.1135 · Cited by 18
Motion Planning Explorer: Visualizing Local Minima Using a Local-Minima Tree
Orthey, A., Frész, B., & Toussaint, M. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2958524 · Cited by 18
“1) Topological Data Analysis: In topological data analysis [6], we use structures from algebraic topology (e.”
MethodThe better turbulence index? Forecasting adverse financial markets regimes with persistent homology
Baitinger, E., & Flegel, S. (2021) · Financial markets and portfolio management · doi:10.1007/s11408-020-00377-x · Cited by 18
“, image recognition (Perea and Carlsson 2014), periodicity detection (Perea and Harer 2015), protein analysis (Xia andWei 2014) and more.11 In the 1990s and early 2000s, advances in topological methods and computing power led to an increasing application of topological tools on real datasets and time series and by thi…”
InfluentialSurvival of Virus Particles in Water Droplets: Hydrophobic Forces and Landauer’s Principle
Bormashenko, E. Y., Fedorets, A. A., Dombrovsky, L. A., et al. (2021) · Entropy · doi:10.3390/e23020181 · Cited by 18
“Computational methods that retrieve correlations in large datasets have been recently developed [25].”
MethodServis, M. J., Sadhu, B., Soderholm, L., et al. (2021) · Journal of Molecular Liquids · doi:10.1016/j.molliq.2021.117743 · Cited by 18
Parallel Computation of Persistent Homology using the Blowup Complex
Lewis, R. H., & Morozov, D. (2015) · ACM Symposium on Parallelism in Algorithms and Architectures (SPAA) · doi:10.1145/2755573.2755587 · Cited by 18
“We refer the reader to the several excellent surveys [9, 10, 11, 12, 13] for a more detailed look at what makes persistence so exciting both to mathematicians and practitioners.”
BackgroundA Multicover Nerve for Geometric Inference
Sheehy, D. R. (2012) · Cited by 18
From numerics to combinatorics: a survey of topological methods for vector field visualization
Wang, W., Wang, W., & Li, S. (2016) · Journal of Visualization · doi:10.1007/s12650-016-0348-8 · Cited by 18
“2002) is an importance evaluation tool for topological features of scalar fields, and is of great potential in big data analysis (Carlsson 2009).”
MethodTopological Data Analysis (TDA) for Time SeriesPreprint
Ravishanker, N., & Chen, R. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.10604 · Cited by 33
“Using computational topology, TDA aims at analyzing topological features of data and representing these features using low dimensional representations (Carlsson, 2009).”
BackgroundMethodApplications of Artificial Intelligence and Big Data in Industry 4.0 Technologies
Rezazadegan, R., & Sharifzadeh, M. (2022) · Wiley eBooks · doi:10.1002/9781119695868.ch5 · Cited by 19
Topological data analysis and topological deep learning beyond persistent homology: a review
Su, Z., Liu, X., Hamdan, L. B., Maroulas, V., Wu, J., Carlsson, G., & Wei, G.-W. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11462-w · Cited by 38
Self-citationObject Oriented Models vs. Data Analysis – Is This the Right Alternative?
Jost, J. (2017) · Boston studies in the philosophy of science · doi:10.1007/978-3-319-54469-4_14 · Cited by 17
The Gromov–Hausdorff distance between spheres
Lim, S., Mémoli, F., & Smith, Z. (2023) · Geometry & Topology · doi:10.2140/gt.2023.27.3733 · Cited by 17
“The Gromov-Hausdorff distance is also a natural choice for expressing the stability of invariants in applied algebraic topology [5, 6, 7] and has also been invoked in applications related to shape matching [3, 23, 24] as a notion of dissimilarity between shapes.”
BackgroundSritharan, D., Wang, S., & Hormoz, S. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2100473118 · Cited by 17
“Recent methods have emerged for estimating homology [16, 17], metrics [14] and geodesics [18] from noisy, sampled data, with accompanying statistical guarantees [18, 19, 20].”
MethodA Primer on Topological Data Analysis to Support Image Analysis Tasks in Environmental Science
Hoef, L. V., Adams, H., King, E. J., & Ebert‐Uphoff, I. (2022) · Artificial Intelligence for the Earth Systems · doi:10.1175/aies-d-22-0039.1 · Cited by 17
Stable volumes for persistent homology
Obayashi, I. (2023) · Journal of Applied and Computational Topology · doi:10.1007/s41468-023-00119-8 · Cited by 20
“Topological data analysis (TDA)[13, 5] is a data analysis method utilizing the mathematical concept of topology.”
MethodTopological packing statistics of living and nonliving matter
Skinner, D. J., Jeckel, H., Martin, A. C., Drescher, K., & Dunkel, J. (2023) · Science Advances · doi:10.1126/sciadv.adg1261 · Cited by 18
“Topology [1] studies the fundamental neighborhood relations amongst living [2–4], non-living [5, 6] or abstract [7, 8] entities.”
BackgroundMethodInfluentialInduction Motor Eccentricity Fault Detection and Quantification Using Topological Data Analysis
Wang, B., Lin, C., Inoué, H., & Kanemaru, M. (2024) · IEEE Access · doi:10.1109/access.2024.3376249 · Cited by 16
“These developments are largely enabled by a powerful tool named persistent homology [15], [21], [22].”
BackgroundMethodMonkam, G. F., Lucia, M. J. D., & Bastian, N. D. (2024) · Computers & Security · doi:10.1016/j.cose.2024.103929 · Cited by 17
Gamma Synchronization Influences Map Formation Time in a Topological Model of Spatial Learning
Basso, E., Arai, M., & Dabaghian, Y. A. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005114 · Cited by 20
“Mathematical methods required for this study are based on Persistent Homology Theory (see [29] and [106, 107]) implemented by the “JPlex” freeware package [108].”
MethodTopological spaces of persistence modules and their properties
Bubenik, P., & Vergili, T. (2018) · Journal of Applied and Computational Topology · doi:10.1007/s41468-018-0022-4 · Cited by 32
“A standard tool in topological data analysis is persistent homology (Ghrist 2008, 2018; Carlsson 2009; Chazal and Michel 2017; Wasserman 2016).”
MethodDissimilarity Space Representations and Automatic Feature Selection for Protein Function Prediction
Santis, E. D., Martino, A., Rizzi, A., et al. (2018) · doi:10.1109/ijcnn.2018.8489115 · Cited by 17
“Further information on Betti numbers and Topological Data Analysis can be found in [32], [33].”
BackgroundAutomatic Detection of Image Morphing by Topology-based Analysis
Jassim, S., & Asaad, A. (2018) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco.2018.8553317 · Cited by 17
“Recent big data applications reveal that data is more complex, noisier, has more missing information but more interestingly has shape [15].”
Background
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
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