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 901–1,000 of 2,331
Motor Eccentricity Fault Detection: Physics-Based and Data-Driven Approaches
Wang, B., Inoué, H., & Kanemaru, M. (2023) · IEEE International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED) · doi:10.1109/sdemped54949.2023.10271414 · Cited by 8
“Topological data analysis (TDA) offers a mathematical tool to extract the shape information from a given data space [20].”
BackgroundDynamic topological data analysis of functional human brain networks
Chung, M. K., Das, S. R., & Ombao, H. (2023) · Foundations of Data Science · doi:10.3934/fods.2023013 · Cited by 11
“When these features are represented as a collection of intervals on the real line, they constitute barcodes [17].”
BackgroundExploring Cybersickness Experiences via a Markov Chain Model
Smith, S. P. (2024) · IEEE Access · doi:10.1109/access.2024.3371516 · Cited by 8
“Topological data analysis [3] was used as the feature extractor to classify participants’ multivariate physiological time series during a virtual-navigation experiment (n = 53).”
MethodBeshkov, K., Fyhn, M., Hafting, T., & Einevoll, G. T. (2024) · iScience · doi:10.1016/j.isci.2024.109370 · Cited by 9
Persistent de Rham-Hodge Laplacians in Eulerian representation for manifold topological learning
Su, Z., Tong, Y., & Wei, G.-W. (2024) · AIMS Mathematics · doi:10.3934/math.20241333 · Cited by 11
“The growth is driven by the great promise of topological approaches to real-world data that are distinguished from any other statistical, mathematical, physical, and engineering methods [11, 42].”
Topological classification of small DC motors
Rucco, M., Concettoni, E., Cristalli, C., et al. (2015) · IEEE International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow · doi:10.1109/rtsi.2015.7325097 · Cited by 8
“This solution is known with witness complexes [12].”
BackgroundHost Trait Prediction of Metagenomic Data for Topology-Based Visualization
Parida, L., Haiminen, N., Haws, D., & Suchodolski, J. S. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-14977-6_8 · Cited by 8
“Recently there has been a rapid growth in interest in TDA and its many applications [24,25,26,27,28].”
BackgroundClustering high dimensional categorical data via topographical features
Chen, C., & Quadrianto, N. (2016) · Sussex Research Online (University of Sussex) · Cited by 8
Persistence barcodes and Laplace eigenfunctions on surfaces
Polterovich, I., Polterovich, L., & Stojisavljević, V. (2018) · Geometriae Dedicata · doi:10.1007/s10711-018-0383-9 · Cited by 8
“We refer the reader to articles [2,9,16,21,44] and monographs [10,15,30] for an introduction into this rapidly developing subject and further details.”
BackgroundExpert Selection in Prediction Markets With Homological Invariants
Bousoño‐Calzón, C., Molina-Bulla, H., Escudero-Garzas, J. J., & Herrera-Galvez, F. J. (2018) · IEEE Access · doi:10.1109/access.2018.2846878 · Cited by 8
“We provide a mathematical formalization in terms of topological invariants calculated from the PM trace data by using Persistent Homology tools [33], and we test it on a controlled experiment consisting of four parallel PMs with real experts in the field of insurance.”
BackgroundMethodCognitive Insights into Sentic Spaces Using Principal Paths
Ragusa, E., Gastaldo, P., Zunino, R., Ferrarotti, M. J., Rocchia, W., & Decherchi, S. (2019) · Cognitive Computation · doi:10.1007/s12559-019-09651-1 · Cited by 8
“First, a proper tool is needed to inspect the topology and properties of data distributions in high-dimensional spaces [21].”
MethodNumerical and analytical analyses of a matrix model with non-pairwise contracted indices
Sasakura, N., & Takeuchi, S. (2020) · The European Physical Journal C · doi:10.1140/epjc/s10052-019-7591-9 · Cited by 13
“Therefore, we have studied topological characters of the configurations that are generated in the simulations by using the modern technique called persistent homology [30] in the topological data analysis.”
MethodInfluentialStatistical Topology and the Random Interstellar Medium
Henderson, R., Makarenko, I., Bushby, P. J., et al. (2019) · Journal of the American Statistical Association · doi:10.1080/01621459.2019.1647841 · Cited by 8
“eld. In our context, there are two topological features of interest, namely components and holes, whose counts in a level set determine the Betti numbers of order zero and one, 0 and 1, respectively (Carlsson 2009). Figure 2 shows four lower-level sets for a simulated eld on a 10 10 lattice and will be used to illustr…”
BackgroundMethodInfluentialTopological IIR Filters Over Simplicial Topologies via Sheaves
Essl, G. (2020) · IEEE Signal Processing Letters · doi:10.1109/lsp.2020.3005548 · Cited by 8
“While the development of topology is predominantly in the realm of pure mathematics [4], topological ideas have increasingly been found useful in applied domains over the last two decades through the emergence of the fields of computational topology [5], applied topology [6], and most recently the direct application o…”
From Geometry to Topology: Inverse Theorems for Distributed Persistence
Solomon, E., Wagner, A., & Bendich, P. (2021) · Journal of computational geometry · doi:10.20382/jocg.v14i2a8 · Cited by 8
Autoencoder networks extract latent variables and encode these variables in their connectomes
Farrell, M., Recanatesi, S., Reid, R. C., Mihalaş, Ş., & Shea‐Brown, E. (2020) · Neural Networks · doi:10.1016/j.neunet.2021.03.010 · Cited by 6
Improved approximate rips filtrations with shifted integer lattices and cubical complexes
Choudhary, A., Kerber, M., & Raghvendra, S. (2021) · Journal of Applied and Computational Topology · doi:10.1007/s41468-021-00072-4 · Cited by 8
“Persistent homology (Carlsson 2009; Edelsbrunner and Harer 2010; Edelsbrunner et al. 2002) is a technique to analyze data sets using topological invariants. The idea is to build a multi-scale representation of data sets and to track its homological changes across the scales. A standard construction for the important c…”
MethodInfluentialSignal Processing on Simplicial Complexes With Vertex Signals
Ji, F. Q., Kahn, G., & Tay, W. P. (2022) · IEEE Access · doi:10.1109/access.2022.3167055 · Cited by 8
“They are already used in topological data analysis [22], representation of surfaces in high dimensions, and modeling of complex networks [23].”
BackgroundAlsaleh, L., Li, C., Couetil, J. L., Ye, Z., Huang, K., Zhang, J., Chen, C., & Johnson, T. S. (2022) · Cancers · doi:10.3390/cancers14194856 · Cited by 8
“Image features were generated by a collaborating lab that specializes in computational pathology and TDA.”
BackgroundInfluentialLin, B. (2022) · Algorithms · doi:10.3390/a15100371 · Cited by 10
“In recent years, technological developments in data visualizations, especially the subfield of topological data anlaysis (TDA), has illuminated the structure of biological data with features like clusters, holes, and skeletons across a range of scales [1].”
BackgroundRunacher, A., Kazemzadeh‐Parsi, M. J., Lorenzo, D. D., et al. (2023) · Polymers · doi:10.3390/polym15061449 · Cited by 8
Preprocessing Network Traffic using Topological Data Analysis for Data Poisoning Detection
Monkam, G. F., Lucia, M. J. D., & Bastian, N. D. (2023) · IEEE Conference on Dependable and Secure Computing (DSC) · doi:10.1109/dsc61021.2023.10354143 · Cited by 8
“In the context of analyzing data, persistent homology emerges as a powerful method for quantifying and identifying the topological characteristics of datasets [5].”
Method$$A_\infty $$ A ∞ -persistence
Guillamon, F. B., & Murillo, A. (2014) · Applicable Algebra in Engineering Communication and Computing · doi:10.1007/s00200-014-0241-4 · Cited by 10
“Persistent homology [4,7,10,22], a topological technique which has proved to be useful in areas such as digital imaging [20], sensor networks [6] or bioinformatics [2], allows to extract global structural information about data sets (specially high dimensional ones) which may contain noise.”
BackgroundComputational homotopy of finite regular CW-spaces
Ellis, G. J., & Hegarty, F. (2013) · Journal of Homotopy and Related Structures · doi:10.1007/s40062-013-0029-4 · Cited by 8
“This is motivated by topics in applied topology such as topological data analysis [1], rigorous numerical analysis of dynamical systems [18], topological robotics [4], image analysis [18] and a range of other applied topics [6,7,12].”
BackgroundFiltrations induced by continuous functions
Fabio, B. D., & Frosini, P. (2013) · Topology and its Applications · doi:10.1016/j.topol.2013.05.013 · Cited by 8
“Then the following statements hold: (1) α(P ) ≤ β(P ) for every P ∈ K.”
BackgroundInfluentialA distributed collapse of a network's dimensionality
Wilkerson, A. C., Chintakunta, H., Krim, H., et al. (2013) · IEEE Global Conference on Signal and Information Processing · doi:10.1109/globalsip.2013.6736948 · Cited by 8
“In addition, even though it doesn’t perfectly model the coverage region of S in general, it can be shown that for rs = rc 2 , Č(S, rs) ⊂ R(S, rc) ⊂ Č(S, rc) [5].”
BackgroundPersistent Homology Analysis of RNA
Mamuye, A. L., Rucco, M., Tesei, L., et al. (2016) · Computational and Mathematical Biophysics · doi:10.1515/mlbmb-2016-0002 · Cited by 8
“Betti numbers are special counters for n-dimensional holes [6].”
BackgroundMethodStochastic multiresolution persistent homology kernel
Zhu, X., Vartanian, A., Bansal, M., et al. (2016) · International Joint Conference on Artificial Intelligence · Cited by 8
Mind the Gap: A Study in Global Development Through Persistent Homology
Banman, A., & Ziegelmeier, L. (2018) · Association for Women in Mathematics series · doi:10.1007/978-3-319-89593-2_8 · Cited by 7
“For foundational material and overviews of computational homology in the setting of persistence, see [5, 6, 8, 2, 17].”
BackgroundFinding Ultrametricity in Data using Topology
Bradley, P. E. (2017) · Journal of Classification · doi:10.1007/s00357-017-9228-8 · Cited by 8
“In fact, the Vietoris-Rips graph is the 1-skeleton of the Vietoris-Rips complex used in topological data analysis (Carlsson, 2009).”
MethodA Survey of Topological Data Analysis (TDA) Methods Implemented in Python
Ray, J. S., & Trovati, M. (2017) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-319-65636-6_54 · Cited by 8
Duman, A. N., Yilbas, B. S., Pirim, H., et al. (2017) · Coatings · doi:10.3390/coatings7090139 · Cited by 8
Signal Analysis With Frame Theory And Persistent Homology
Boche, H., Guillemard, M., Kutyniok, G., & Philipp, F. (2013) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.54409 · Cited by 9
A Numerical Approach for the Filtered Generalized Čech Complex
Espinoza, J. F., Hernández-Amador, R., Hernández, H. A., & Ramonetti-Valencia, B. (2019) · Algorithms · doi:10.3390/a13010011 · Cited by 8
“dition, we show how the algorithm yields the minimal enclosing ball for a nite set of points in the plane. 1. Introduction Recently, in the study of data point clouds from a topological approach (cf. [4, 5, 11, 16, 24]) the need to develop algorithms to construct dierent simplicial structures has arisen, such as the V…”
BackgroundTopological data analysis reveals parameters with prognostic skill for extreme wildfire size
Bendick, R. O., & Hoylman, Z. (2020) · Environmental Research Letters · doi:10.1088/1748-9326/aba8c2 · Cited by 8
“As briefly summarized in the introduction, the TDA approach identifies relationships among N parameters by calculating an N-dimensional topology for large data sets using distancemeasures of similarity (Carlsson 2009, Lum et al 2013).”
MethodFrom Topological Analyses to Functional Modeling: The Case of Hippocampus
Dabaghian, Y. A. (2021) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2020.593166 · Cited by 8
“…Article 593166 the Betti numbers of a space or a complex X is known as its topological barcode, b(T ) = (b0(T ), b1(T ), b2(T ), . . .), which captures the topological identity of T (Zomorodian, 2005; Zomorodian and Carlsson, 2005; Ghrist, 2008; Carlsson, 2009, 2013; Edelsbrunner and Harer, 2010).”
BackgroundData Analysis Using Representation Theory and Clustering Algorithms
Alkhushayni, S. M., Choi, T., & Al-Zaleq, D. (2021) · WSEAS Transactions on Computers archive · doi:10.37394/23205.2020.19.38 · Cited by 8
“Persistence Homology shows us which groups "persist" as they absorb other clusters, and it gives a good idea of what scales cause a change in the clusters in the data [1].”
BackgroundStable topological signatures for metric trees through graph approximations
Vandaele, R., Rieck, B., Saeys, Y., & Bie, T. D. (2021) · Pattern Recognition Letters · doi:10.1016/j.patrec.2021.03.035 · Cited by 8
Time Series Clustering with Topological and Geometric Mixed Distance
Zhang, Y., Shi, Q., Zhu, J., Peng, J., & Li, H.-F. (2021) · Mathematics · doi:10.3390/math9091046 · Cited by 9
“However, these global methods based on TDA are only able to extract qualitative information from the time series and cannot take into account local quantitative information [19].”
MethodCerebellar connectivity maps embody individual adaptive behavior in mice
Spaeth, L., Bahuguna, J., Gagneux, T., Dorgans, K., Sugihara, I., Poulain, B., Battaglia, D. A., & Isope, P. (2022) · Nature Communications · doi:10.1038/s41467-022-27984-8 · Cited by 12
Computing persistent homology under random projection
Ramamurthy, K. N., Varshney, K. R., & Thiagarajan, J. J. (2014) · IEEE Statistical Signal Processing Workshop (SSP) · doi:10.1109/ssp.2014.6884586 · Cited by 9
“the Betti number βk(S) is the dimension of the k homology group of the complex, Hk(S) [4].”
BackgroundPersistence Terrace for Topological Inference of Point Cloud Data
Moon, C., Giansiracusa, N., & Lazar, N. A. (2018) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2017.1422432 · Cited by 8
“rvation; rather, it can now be an entire image, or a histogram, or a function. A relatively new, emergent approach is topological data analysis (TDA), which focuses on the \shape" of a data set (Carlsson, 2009; Lum et al., 2013). A popular source of data for successful applications of TDA has been biology; exampl…”
BackgroundClassifying Clustering SchemesPreprint
Carlsson, G., & Mémoli, F. (2010) · arXiv (Cornell University) · doi:10.48550/arxiv.1011.5270 · Cited by 8
“It is also true that functoriality is necessary to extend the methods of clustering to higher order connectivity information with computational topology of finite datasets [3].”
BackgroundMethodSelf-citationAutoencoding topologyPreprint
Korman, E. O. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.00156 · Cited by 8
“This is very similar, and motivated by, the construction of barcodes in persistent homology [2].”
BackgroundFülöp, T., Desroches, M., Santos, F. A. N., & Rodrigues, S. (2020) · arXiv (Cornell University) · Cited by 8
Failure Filtrations for Fenced Sensor Networks ∗Preprint
Munch, E., Shapiro, M., & Harer, J. (2011) · Cited by 8
Bull, J. A., Mulholland, E. J., Moore, J. W., Bosque, J. J., Stolz, B. J., Boen, J., Eggington, H. R., Belnoue-Davis, H. L., Jones, H. J. S., Gatenbee, C., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.06.02.597010 · Cited by 11
“…) = 𝐾𝑒𝑟(𝜕 0 ) 𝐼𝑚(𝜕 1 ) , (13) The inclusion maps between the sublevel sets 𝑋 𝑡 1 ⊆ 𝑋 𝑡 2 induce maps between their homology groups 𝐻 𝑖 (𝑋 𝑡 1 ) and 𝐻 𝑖 (𝑋 𝑡 2 ) which allow the construction of a persistence diagram to track the elements of the vector spaces across the filtration.”
BackgroundMethodKanari, L., Schmidt, S., Casalegno, F., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.09.13.612635 · Cited by 8
“We argued in Section 4.3.2 that TMD is a method that captures topological properties of graph data effectively and reduces them to a two-dimensional representation in the form of barcodes, persistence diagrams (Carlsson, 2009), and persistence images (Adams et al., 2015).”
InfluentialRutkowski, T. M., Komendziński, T., & Otake, M. (2024) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2023.1294139 · Cited by 7
“Among the many topological methods developed for data analysis (Carlsson, 2009; Patania et al., 2017; Perea, 2018), a persistent homology is the most often used (Varley et al., 2021).”
BackgroundMethodTowards Analysis of Multivariate Time Series Using Topological Data Analysis
Zheng, J., Feng, Z., & Ekstrom, A. D. (2024) · Mathematics · doi:10.3390/math12111727 · Cited by 7
Morphological multiparameter filtration and persistent homology in mitochondrial image analysis
Chung, Y.-M., Hu, C.-S., Sun, E., & Tseng, H. C. (2024) · PLoS ONE · doi:10.1371/journal.pone.0310157 · Cited by 7
Du, G., Zhou, L., Huang, Y., Wu, Y., & Hong, Z. (2024) · Acta Materialia · doi:10.1016/j.actamat.2024.120467 · Cited by 8
Using topological data analysis and machine learning to predict customer churn
Sagming, M., Heymann, R., & Visaya, M. V. (2024) · Journal Of Big Data · doi:10.1186/s40537-024-01020-6 · Cited by 7
A distribution-guided Mapper algorithm
Tao, Y., & Ge, S.-F. (2024) · BMC Bioinformatics · doi:10.1186/s12859-025-06085-5 · Cited by 10
“We introduce the following notions to describe the nerve theorem [13, 14].”
Rodrigues, J. A. (2025) · International Journal of Topology · doi:10.3390/ijt2020008 · Cited by 7
Topological Analysis of Syntactic Structures
Port, A., Karidi, T., & Marcolli, M. (2022) · Mathematics in Computer Science · doi:10.1007/s11786-021-00520-5 · Cited by 7
“igh dimensional and complex data sets and identify the topological structures the data exhibit. In particular, persistent homology is a fundamental tool at the heart of topological data analysis, see [7] and also [4], [10], [14], [42]. Persistent homology gained much popularity in recent years as the primary method of…”
BackgroundMethodDelay Parameter Selection in Permutation Entropy Using Topological Data Analysis
Myers, A., Chumley, M. M., & Khasawneh, F. A. (2024) · La Matematica · doi:10.1007/s44007-024-00110-4 · Cited by 7
“[9] suggest τ = 2 or 3 and n ∈ [3, 7] for heart rate applications.”
BackgroundUniversity Academic Performance Development Prediction Based on TDA
Yu, D., Zhou, X., Pan, Y. R., Niu, Z., Xu, Y., & Sun, H. (2022) · Entropy · doi:10.3390/e25010024 · Cited by 8
“Although one can trace back geometric methods used for data analysis long ago, TDA really started as a field with the pioneering works of Edelsbrunner et al. [34] and Zomorodian and Carlsson [35] in persistent homology and was popularized in a landmark paper by Carlsson [36].”
BackgroundEnergy-based clustering: Fast and robust clustering of data with known likelihood functions.
Thürlemann, M., & Riniker, S. (2023) · Journal of Chemical Physics · doi:10.1063/5.0148735 · Cited by 5
Quantifying the clusterness and trajectoriness of single-cell RNA-seq data
Lim, H. S., & Qiu, P. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011866 · Cited by 6
Lower Bounds on the Homology of Vietoris–Rips Complexes of Hypercube Graphs
Adams, H., & Virk, Ž. (2024) · Bulletin of the Malaysian Mathematical Sciences Society · doi:10.1007/s40840-024-01663-x · Cited by 7
“Originally introduced for use in algebraic topology [24] and geometric group theory [8, 17], Vietoris–Rips complexes are now a commonly-used tool in applied and computational topology in order to approximate the shape of a dataset [9, 13].”
Statistical Analysis of Quantitative Cancer Imaging Data
Mohammed, S., Masotti, M., Osher, N., Acharyya, S., & Baladandayuthapani, V. (2024) · Statistics and data science in imaging. · doi:10.1080/29979676.2024.2405348 · Cited by 7
Schäfer, M., Hildenbrand, G. L., & Hausmann, M. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms252312843 · Cited by 9
Robust Satisfiability of Systems of Equations
Franek, P., & Krčál, M. (2015) · Journal of the ACM · doi:10.1145/2751524 · Cited by 7
“…FRANEK, Institute of Computer Science, ASCR Prague MAREK KRČÁL, IST Austria, Klosterneuburg We study the problem of robust satisfiability of systems of nonlinear equations, namely, whether for a given continuous function f : K → Rn on a finite simplicial complex K and α > 0, it holds…”
BackgroundMethodFailure filtrations for fenced sensor networks
Munch, E., Shapiro, M., & Harer, J. (2012) · The International Journal of Robotics Research · doi:10.1177/0278364912451671 · Cited by 7
“(In Figure [3] we have not subdivided the rectangles to keep the picture uncluttered.”
BackgroundRandom cyclic dynamical systems
Adamaszek, M., Adams, H., & Motta, F. (2016) · Advances in Applied Mathematics · doi:10.1016/j.aam.2016.08.007 · Cited by 7
“In applications of computational topology [9, 8] one would like to compute with Vietoris–Rips complexes, even though the number of simplices of VR(X; r) can be exponential in |X|.”
MethodUltrametricity indices for the Euclidean and Boolean hypercubes
Bradley, P. E. (2016) · P-Adic Numbers Ultrametric Analysis and Applications · doi:10.1134/s2070046616040038 · Cited by 7
Dimension Estimation and Topological Manifold Learning
Tasaki, H., Lenz, R., & Chao, J. (2019) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2019.8852081 · Cited by 7
“Another recent trend is to use topological models for image processing and recognition (for example [16], [17]).”
BackgroundMultivariate central limit theorems for random simplicial complexes
Akinwande, G., & Reitzner, M. (2020) · Advances in Applied Mathematics · doi:10.1016/j.aam.2020.102076 · Cited by 7
“For an introduction to this we refer to the surveys on persistent homology by Carlsson [1] and Ghrist [3].”
MethodMining Topological Structure in Graphs through Forest Representations
Vandaele, R., Saeys, Y., & Bie, T. D. (2020) · Ghent University Academic Bibliography (Ghent University) · Cited by 8
Event history and topological data analysis
Garside, K., Gjoka, A., Henderson, R., Johnson, H., & Makarenko, I. (2020) · Biometrika · doi:10.1093/biomet/asaa097 · Cited by 7
Randomized Self-Organizing Map
Rougier, N. P., & Detorakis, G. (2021) · Neural Computation · doi:10.1162/neco_a_01406 · Cited by 9
“Topological data analysis (TDA; Carlsson, 2009) provides methods and tools to study topological structures of data sets such as point cloud and is useful when geometrical or topological information is not apparent within a data set.”
BackgroundMethodPersistent homology for hypergraphs and computational tools — A survey for users
Ren, S. (2020) · Journal of Knot Theory and Its Ramifications · doi:10.1142/s0218216520430075 · Cited by 7
Arioli, A., Dagliati, A., Geary, B., Peek, N. B., Kalra, P. A., Whetton, A. D., & Geifman, N. (2021) · PLoS ONE · doi:10.1371/journal.pone.0249771 · Cited by 9
“The TDA Mapper’s output is a network with intrinsic topological features that represents the shape of data [19] in such a way that every node in a network is a cluster of instances (e.”
BackgroundPeriod and signal reconstruction from the curve of trains of samples
Rupniewski, M. W. (2021) · IET Signal Processing · doi:10.1049/sil2.12086 · Cited by 7
“[3] Gunnar Carlsson, “Topology and data,” Bulletin of the American Mathematical Society, vol. 46, no. 2, pp. 255– 308, 2009.”
MethodEndo, S., & Yokogawa, S. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3130570 · Cited by 7
Enhanced head-skull shape learning using statistical modeling and topological features
Nguyen, T., Tran, V. D., Nguyen, H. Q., et al. (2022) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-021-02483-y · Cited by 7
𝓗1 persistent features of the resting-state connectome in healthy subjects
Martínez, D., Gonzalez, F. A., & Gómez, F. (2022) · Network Neuroscience · doi:10.1162/netn_a_00280 · Cited by 7
“These descriptors correspond to the number of loops, voids, and in general, cavities (Carlsson, 2009; Edelsbrunner & Harer, 2010), summarizing the topological properties of data.”
BackgroundTopological Data Analysis for Image-based Machine Learning: Application to Electric Motors
Wang, B., Talukder, A. K. M. K. A., & Sakamoto, Y. (2022) · International Conference on Electrical Machines (ICEM) · doi:10.1109/icem51905.2022.9910734 · Cited by 7
“Topology is a branch of mathematics to deal with qualitative geometric information [15], which studies the connected components of a space, such as the classification of loops and higher dimensional surfaces within the space.”
BackgroundBiomolecular Topology: Modelling and Analysis
Liu, J., Xia, K., Wu, J., Yau, S. S. ‐., & Wei, G. (2022) · Acta Mathematica Sinica English Series · doi:10.1007/s10114-022-2326-5 · Cited by 9
“With the ever-increasing data, the exceptional power of topology in dimensionality and complexity reduction has attracted enormous attention [38, 69].”
BackgroundConvergence of persistence diagram in the sparse regime
Owada, T. (2022) · The Annals of Applied Probability · doi:10.1214/22-aap1800 · Cited by 8
“In the special case α = 1, it reduces to the sum of persistence barcodes [18, 7].”
BackgroundOhki, T., Kunii, N., & Chao, Z. C. (2023) · Reviews in the Neurosciences · doi:10.1515/revneuro-2022-0137 · Cited by 7
Venkat, A., Bhaskar, D., & Krishnaswamy, S. (2023) · Trends in Immunology · doi:10.1016/j.it.2023.05.003 · Cited by 7
Topological data analysis reveals differences between simulated galaxies and dark matter haloes
Ouellette, A., Holder, G. P., & Kerman, E. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad1765 · Cited by 8
“An overview of these methods can be found in Carlsson (2009).”
MethodSomasundaram, E., Wadhwa, R., Litzler, A., et al. (2023) · JCO Clinical Cancer Informatics · doi:10.1200/cci.22.00173 · Cited by 7
Non-stenotic fibro-calcific aortic valve as a predictor of myocardial infarction recurrence
Myasoedova, V. A., Chiesa, M., Cosentino, N., et al. (2024) · European Journal of Preventive Cardiology · doi:10.1093/eurjpc/zwae062 · Cited by 7
Generic families of finite metric spaces with identical or trivial 1-dimensional persistence
Smith, P. A., & Kurlin, V. A. (2024) · Journal of Applied and Computational Topology · doi:10.1007/s41468-024-00177-6 · Cited by 7
“The key papers of Gunnar Carlsson [5], Robert Ghrist [6], and Shmuel Weinberger [7] were followed by substantial developments of Fred Chazal [8] and others.”
High-dimensional data and linear models: a review
Brimacombe, M. B. (2014) · Open Access Medical Statistics · doi:10.2147/oams.s56499 · Cited by 7
“Data science or big data-based approaches to identifying patterns in these large sets of collected data are quite varied, often reflecting a mix of methods drawn from engineering, computer science, and mathematics.(15) The statistically based approaches reviewed here also apply to these areas of investigation.”
BackgroundOn the Fusion of Physics and Klein Bottle Logic in Biology, Embryogenesis and Evolution
Rapoport, D. L. (2011) · NeuroQuantology · doi:10.14704/nq.2011.9.4.505 · Cited by 7
Comparison of the dynamics of active regions by methods of computational topology
Knyazeva, I., Makarenko, N. G., & Urt’ev, F. A. (2015) · Geomagnetism and Aeronomy · doi:10.1134/s0016793215080150 · Cited by 7
Matroids And Canonical Forms: Theory And Applications
Henselman, G. (2017) · ScholarlyCommons (University of Pennsylvania) · Cited by 7
Ricci curvature and the manifold learning problem
Ache, A. G., & Warren, M. W. (2018) · Advances in Mathematics · doi:10.1016/j.aim.2018.11.001 · Cited by 7
“Although since the publication of the seminal paper by Carlsson [7] there has been an explosion of interest in topological data analysis, cf.”
BackgroundSampling Real Algebraic Varieties for Topological Data Analysis
Dufresne, E., Edwards, P. B., Harrington, H. A., & Hauenstein, J. D. (2019) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2019.00253 · Cited by 10
“We apply the persistent homology pipeline popularized by Carlsson in [16] and summarized by Ghrist in [31].”
MethodBetti number ratios as quantitative indices for bone morphometry in three dimensions
Teramoto, T., Kamiya, T., Sakurai, T., et al. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.05.012 · Cited by 7
Topological data analysis to improve exemplar-based inpainting
Jassim, S., Al-Jaberi, A. K., Asaad, A., et al. (2018) · doi:10.1117/12.2309931 · Cited by 7
“These three properties are coordinate free, invariant under deformation and compressed representation which explained in detail in [18].”
BackgroundConvergence of persistence diagrams for topological crackle
Owada, T., & Bobrowski, O. (2020) · Bernoulli · doi:10.3150/20-bej1193 · Cited by 8
“We suggest [13, 21] as a good introductory reading, while a more rigorous coverage of algebraic topology is in [23].”
MethodEvidence for Synchronization in the Global Earthquake Catalog
Bendick, R. O., & Mencin, D. J. (2020) · Geophysical Research Letters · doi:10.1029/2020gl087129 · Cited by 7
“Such methods are highly effective at identifying groups of events that share many characteristics or have unique properties, even if other measured properties are not sorted in the same way or are distributed stochastically (Carlsson, 2009).”
MethodTopological Signal Processing in Neuroimaging Studies
Wang, Y., Behroozmand, R., Johnson, L. P., et al. (2020) · doi:10.1109/isbiworkshops50223.2020.9153363 · Cited by 7
“In recent decades, the abstraction of algebraic topology has given rise to TDA - a set of computational algorithms that extract topological features in complex data objects that cannot be easily decoded by standard analytic tools [5].”
BackgroundPolitical structures and the topology of simplicial complexes
Mock, A., & Volić, I. (2021) · Mathematical Social Sciences · doi:10.1016/j.mathsocsci.2021.09.004 · Cited by 7
Application of Topological Data Analysis to Multi-Resolution Matching of Aerosol Optical Depth Maps
Ofori-Boateng, D., Lee, H., Gorski, K. M., et al. (2021) · Frontiers in Environmental Science · doi:10.3389/fenvs.2021.684716 · Cited by 7
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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