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.”
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Cited by
Sorted by citations
Items 401–500 of 2,331
Teramoto, T., Shinohara, T., & Takiyama, A. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105614 · Cited by 30
Topological data analysis model for the spread of the coronavirus
Chen, Y., & Volić, I. (2021) · PLoS ONE · doi:10.1371/journal.pone.0255584 · Cited by 30
Signal Processing On Cell Complexes
Roddenberry, T. M., Schaub, M. T., & Hajij, M. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9747233 · Cited by 32
Statistical Persistent Homology of Brain Signals
Wang, Y., Ombao, H., & Chung, M. K. (2019) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2019.8682978 · Cited by 30
“Topological data analysis (TDA) extracts topological and geometric features in complex data that cannot be easily decoded by standard analytic tools [1].”
BackgroundPersistent homology analysis of ion aggregations and hydrogen-bonding networks
Xia, K. (2018) · Physical Chemistry Chemical Physics · doi:10.1039/c8cp01552j · Cited by 33
“Persistent Betti functions Based on the persistent homology results, different functions are proposed to represent or analyze the topological information.(40,60,75,76) The persistent Betti number (PBN) is one of them, it is defined as the summation of all the k-th dimensional barcodes,”
BackgroundOesterling, P., Scheuermann, G., Teresniak, S., Heyer, G., Koch, S., Ertl, T., & Weber, G. H. (2010) · IEEE Conference on Visual Analytics Science and Technology (VAST) · doi:10.1109/vast.2010.5652940 · Cited by 30
“A good survey through this field is given in [2].”
BackgroundCrawford, L. A., Monod, A., Chen, A., et al. (2019) · Figshare · doi:10.6084/m9.figshare.10269596.v1 · Cited by 30
Wong, E., Palande, S., Wang, B., et al. (2016) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2016.7493506 · Cited by 29
“, [2] for seminal work on the topic and [3, 4] for excellent surveys.”
BackgroundPersistent homology and applied homotopy theory
Carlsson, G. (2020) · Chapman & Hall/CRC eBooks · doi:10.1201/9781351251624-8 · Cited by 30
“el, so that the reader can orient him/herself in the techniques. We remark that there are a number of useful surveys on persistent homology and on topological data analysis more generally. The papers [19], [36], and [55] give dierent perspectives on this subject. 2 The Motivating Problem Suppose that we are given a se…”
BackgroundSelf-citationMultidimensional Persistence and Noise
Scolamiero, M., Chachólski, W., Lundman, A., Ramanujam, R., & Öberg, S. (2016) · Foundations of Computational Mathematics · doi:10.1007/s10208-016-9323-y · Cited by 48
“ological data analysis. In order to build these geometric objects, one needs to be given measurements on the data set. The measurements, using for example the Cech or Vietoris-Rips constructions (see [1]),ˇ lead to a functor X: Qr→Spaces, indexed by the poset of r-tuples of non-negative rational numbers Qr (see 2.6),…”
MethodInfluentialMachine Learning Augmented Echocardiography for Diastolic Function Assessment
Fletcher, A. J., Lapidaire, W., & Leeson, P. (2021) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2021.711611 · Cited by 29
“The TDA technique is well-suited for detecting subtle geometric patterns in high dimensional data (57, 58).”
MethodTopological descriptor of thermal conductivity in amorphous Si
Minamitani, E., Shiga, T., Kashiwagi, M., et al. (2022) · The Journal of Chemical Physics · doi:10.1063/5.0093441 · Cited by 29
Cell cycle controls long-range calcium signaling in the regenerating epidermis
Moore, J. L., Bhaskar, D., Gao, F., Matte-Martone, C. C., Du, S., Lathrop, E., Ganesan, S., Shao, L., Norris, R. P., Sanz, N. C., et al. (2023) · The Journal of Cell Biology · doi:10.1083/jcb.202302095 · Cited by 28
“…data is gradually coarse-grained by merging nearby points, and at each level of granularity, a graph (or simplicial complex in higher dimensions) of the data is quantified by counting the number of connected components, cycles, and potentially higher-dimensional “ holes ” (Carlsson, 2009).”
BackgroundDynamical Emergence Theory (DET): A Computational Account of Phenomenal Consciousness
Moyal, R., Fekete, T., & Edelman, S. (2020) · Minds and Machines · doi:10.1007/s11023-020-09516-9 · Cited by 26
“…it is interesting to note that it is possible to reconstruct the topology of a dynamical system from “point cloud” (as opposed to temporal sequence) data, if a distance function over points is available (e.g., Singh et al. 2008; Carlsson 2009; cf. Fekete 2010 and Fekete and Edelman 2011).”
BackgroundEnhancing financial time series forecasting through topological data analysis
de Jesus, L. C., Fernández‐Navarro, F., & Carbonero-Ruz, M. (2025) · Neural Computing and Applications · doi:10.1007/s00521-024-10787-x · Cited by 28
“Topological data analysis (TDA) utilizes concepts from algebraic topology to analyse the shape of data, making it a pivotal tool for extracting meaningful insights from complex datasets [1].”
A Review of Topological Data Analysis and Topological Deep Learning in Molecular Sciences
Wee, J., & Jiang, J. W. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c02266 · Cited by 36
“Many challenges for conventional molecular data analyzes can be effectively addressed by topological data analysis (TDA) [22], an emerging field in data science and a new research frontier in applied sciences.”
Topology driven modeling: the IS metaphor
Merelli, E., Pettini, M., & Rasetti, M. (2014) · Natural Computing · doi:10.1007/s11047-014-9436-7 · Cited by 29
“…notion of data space, its geometry and topology are the most natural tools to handle the unprecedentedly large, high-dimensional, complex sets of data (Carlsson 2009; Edelsbrunner and Harer 2010); basic ingredient of the new data-driven complexity science (TOPDRIM 2012; Merelli and Rasetti 2013).”
BackgroundEdit Distance and Persistence Diagrams over Lattices
McCleary, A., & Patel, A. (2022) · SIAM Journal on Applied Algebra and Geometry · doi:10.1137/20m1373700 · Cited by 28
“e we develop here and our main theorem is of interest to topological data analysis. Ordinary 1-parameter persistent homology is used to study the shape of data, but it is highly sensitive to outliers [5]. It is natural to want to penalize outliers, but then this typically results in a multiparameter ltration [6]. Mult…”
BackgroundObservation of fractional spin textures in a Heusler material
Jena, J., Göbel, B., Hirosawa, T., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-29991-1 · Cited by 28
Hypergraph Artificial Benchmark for Community Detection (h–ABCD)
Kamiński, B., Prałat, P., & Théberge, F. (2023) · Journal of Complex Networks · doi:10.1093/comnet/cnad028 · Cited by 30
An Overview of data science uses in bioimage informatics
Chessel, A. (2017) · Methods · doi:10.1016/j.ymeth.2016.12.014 · Cited by 30
Persistent homology of unweighted complex networks via discrete Morse theory
Kannan, H., Saucan, E., Roy, I., & Samal, A. (2019) · MPG.PuRe (Max Planck Society) · Cited by 27
An introduction to persistent homology for time series
Равишанкер, Н., & Chen, R. (2021) · Wiley Interdisciplinary Reviews Computational Statistics · doi:10.1002/wics.1548 · Cited by 28
“Excellent references are Carlsson (2009) and Edelsbrunner and Harer (2010).”
BackgroundIacopini, I., Foote, J. R., Fefferman, N. H., Derryberry, E. P., & Silk, M. J. (2024) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2023.0190 · Cited by 26
“Readers interested in exploiting the recent advances in TDA to study higher-order communication landscapes can read (29).”
BackgroundPersistent homology on LiDAR data to detect landslides
Syzdykbayev, M., Karimi, B., & Karimi, H. A. (2020) · Remote Sensing of Environment · doi:10.1016/j.rse.2020.111816 · Cited by 27
Tracking collective cell motion by topological data analysis
Bonilla, L. L., Carpio, A., & Trenado-Yuste, C. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008407 · Cited by 27
“Persistent features are more likely to represent true features of the data/pattern under study than to constitute artifacts of sampling, noise, or parameter choice [50].”
BackgroundMethodCoexistence holes characterize the assembly and disassembly of multispecies systems
Angulo, M. T., Kelley, A., Montejano, L., Song, C., & Saavedra, S. (2021) · Nature Ecology & Evolution · doi:10.1038/s41559-021-01462-8 · Cited by 24
Abbaspourazad, H., Erturk, E., Pesaran, B., & Shanechi, M. M. (2023) · Nature Biomedical Engineering · doi:10.1038/s41551-023-01106-1 · Cited by 40
“Because the latent trajectories in DFINE were more predictive of neural activity and behaviour, we used visualization and TDA analyses on these trajectories to study the latent manifold structure in data and found these trajectories to reveal a ring-like manifold structure (Fig.”
InfluentialPort, A., Gheorghita, I., Guth, D. J., Clark, J. M., Liang, C., Dasu, S., & Marcolli, M. (2017) · Mathematics in Computer Science · doi:10.1007/s11786-017-0329-x · Cited by 26
“An important and fast developing area of data analysis, in recent years, has been the study of high dimensional structures in large sets of data points, via topological methods, see [20–22].”
BackgroundMethodTopological analysis and visualisation of network monitoring data: Darknet case study
Coudriau, M., Lahmadi, A., & François, J. (2016) · IEEE International Workshop on Information Forensics and Security · doi:10.1109/wifs.2016.7823920 · Cited by 27
“The approach relies on Topological Data Analysis (TDA) [5] methods applied to the packet-level data to find persistent structures and patterns.”
MethodOffset hypersurfaces and persistent homology of algebraic varieties
Horobeţ, E., & Weinstein, M. (2019) · Computer Aided Geometric Design · doi:10.1016/j.cagd.2019.101767 · Cited by 29
“A classical way to do this is by persistent homology ([4, 16, 22]), which studies the homology of the set of points within a range of distances from the data set, and considers features to be of interest if they persist through a wide range of the distance parameter.”
BackgroundBartholdi, L., Schick, T., Smale, N., & Smale, S. (2011) · Foundations of Computational Mathematics · doi:10.1007/s10208-011-9107-3 · Cited by 26
“s α → 0 one recovers the classical setting. A closely related point of view is that of persistent homology, see Edelsbrunner, Letscher, and Zomorodian [17], Zomorodian and Carlsson [40], and Carlsson [6]. We give a setting for such a scaled theory, with a fixed scaling parameter α > 0. Let X be a separable, complete…”
BackgroundPersistent Topological Laplacians—A Survey
Wei, X., & Wei, G. (2025) · Mathematics · doi:10.3390/math13020208 · Cited by 28
“Although there are numerous reviews and monographs on persistent homology [1,74–76], there is no review on persistent topological Laplacians.”
Hole detection in metabolic connectivity of Alzheimer's disease using kappa-Laplacian.
Lee, H., Chung, M. K., Kang, H., & Lee, D. S. (2014) · PubMed · Cited by 26
Discovering the Whole by the Coarse: A topological paradigm for data analysis
Krim, H., Gentimis, T., & Chintakunta, H. (2016) · IEEE Signal Processing Magazine · doi:10.1109/msp.2015.2510703 · Cited by 26
“This article, owing to its intentionally limited scope, skips the practically important and mathematically rigorous concept of persistent homology [3].”
BackgroundSpatiotemporal Persistent Homology for Dynamic Metric Spaces
Kim, W., & Mémoli, F. (2020) · Discrete & Computational Geometry · doi:10.1007/s00454-019-00168-w · Cited by 40
“In [64], Halverson, Topaz and Ziegelmeier study aggregation models for biological systems by adopting ideas from TDA.”
BackgroundInfluentialTopological learning for brain networks
Songdechakraiwut, T., & Chung, M. K. (2023) · The Annals of Applied Statistics · doi:10.1214/22-aoas1633 · Cited by 32
“Long-lived features persist over a long range of resolutions and are considered as signal while short-lived features are considered as noise (Carlsson, 2009).”
BackgroundPersistent hyperdigraph homology and persistent hyperdigraph Laplacians
Chen, D., Liu, J., Wu, J., & Wei, G. (2023) · Foundations of Data Science · doi:10.3934/fods.2023010 · Cited by 27
“Through comparative analysis, persistent homology can be used to infer the shape of data [7].”
BackgroundZhang, R., Wang, R., & Ng, T.-T. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-32205-1_24 · Cited by 27
A Fractal Dimension for Measures via Persistent Homology
Adams, H., Aminian, M., Farnell, E., Kirby, M. J. L., Mirth, J., Neville, R., Peterson, C., & Shonkwiler, C. (2020) · Abel symposia · doi:10.1007/978-3-030-43408-3_1 · Cited by 24
“e scale increases. We provide a brief introduction to persistent homology in this preliminaries section, but we point the interested reader to [8,28,39] for thorough introductions to homology, and to [17,23,36] for excellent expository articles on persistent homology. Geometric complexes, which are at the heart of the…”
BackgroundStrong Law of Large Numbers for Betti Numbers in the Thermodynamic Regime
Goel, A., Trinh, K. D., & Tsunoda, K. (2018) · Journal of Statistical Physics · doi:10.1007/s10955-018-2201-z · Cited by 26
Cubical Ripser: Software for computing persistent homology of image and volume dataPreprint
Kaji, S., Sudo, T., & Ahara, K. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.12692 · Cited by 26
“Recent years, Topological Data Analysis (TDA, for short) has gained much attention as a new way of looking at data ([4, 8] provide a survey).”
BackgroundVandaele, R., Mukherjee, P., Selby, H. M., Shah, R. P., & Gevaert, O. (2022) · Patterns · doi:10.1016/j.patter.2022.100657 · Cited by 26
“Complementary to this, the rising field of topological data analysis (TDA) [4], and in particular its main method, persistent homology [5], provides an unparalleled tool to quantify local to global structural information in data.”
BackgroundInformation Entropy Estimation Based on Point-Set Topology for Hyperspectral Anomaly Detection
Sun, X., Zhuang, L., Gao, L., Gao, H., Sun, X., & Zhang, B. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3424465 · Cited by 27
“Such a research idea encourages the exploration of data features in HSIs at the level of mathematical mechanisms, thus providing more possibilities for the design of detection schemes through theoretical migrations [53], [54], [55], [56], [57].”
BackgroundTopological data analysis of Escherichia coli O157:H7 and non-O157 survival in soils
Ibekwe, A. M., Ma, J., Crowley, D. E., Yang, C., Johnson, A. M., Petrossian, T. C., & Lum, P. Y. (2014) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2014.00122 · Cited by 24
“Chan JM, Carlsson G, Rabadan R (2013). Topology of viral evolution.”
BackgroundMethodInfluential(Hyper)Graph Embedding and Classification via Simplicial Complexes
Martino, A., Giuliani, A., & Rizzi, A. (2019) · Algorithms · doi:10.3390/a12110223 · Cited by 27
“Simplicial complexes [50,51] are properly constructed finite collections of simplices that are closed with respect to inclusions of the faces: if a given simplex s belongs to a given simplicial complex S , then all faces of s also belong to S .”
BackgroundMethodTopological View of Flows Inside the BOLD Spontaneous Activity of the Human Brain
Don, A. P. H., Peters, J. F., Ramanna, S., & Tozzi, A. (2020) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2020.00034 · Cited by 25
“A common approach is to build a continuous shape (graphs) on top of data to detect complex topological and underlying geometric structures (Carlsson, 2009; Chazal and Michel, 2017).”
BackgroundParameter investigation of topological data analysis for EEG signals
Altindış, F., Yılmaz, B., Borisenok, S., & İçöz, K. (2020) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2020.102196 · Cited by 25
Using persistent homology as preprocessing of early warning signals for critical transition in flood
Musa, S. M. S. S., Noorani, M. S. M., Razak, F. A., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-86739-5 · Cited by 25
Salch, A., Regalski, A., Abdallah, H., Suryadevara, R., Catanzaro, M. J., & Diwadkar, V. A. (2021) · PLoS ONE · doi:10.1371/journal.pone.0255859 · Cited by 30
“The maturing field of TDA is now at the leading envelope of attempts to understand and characterize structure within data [7, 8].”
BackgroundA topological perspective on weather regimes
Strømmen, K., Chantry, M., Dorrington, J., et al. (2022) · Climate Dynamics · doi:10.1007/s00382-022-06395-x · Cited by 25
“There are different ways to use topology to study data, see the survey [26] for an overview.”
BackgroundMethodInfluentialKyeong, S.-H., Park, S., Cheon, K.-A. A., Kim, J., Song, D., & Kim, E. J. (2015) · PLoS ONE · doi:10.1371/journal.pone.0137296 · Cited by 26
“A detailed description of these particular clustering procedures can be found in the references [33,36].”
BackgroundNetworks and Cycles: A Persistent Homology Approach to Complex Networks
Petri, G., Scolamiero, M., Donato, I., & Vaccarino, F. (2013) · Springer proceedings in complexity · doi:10.1007/978-3-319-00395-5_15 · Cited by 26
Online HodgeRank on Random Graphs for Crowdsourceable QoE Evaluation
Xu, Q., Xiong, J., Huang, Q., & Yao, Y. (2014) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2013.2292568 · Cited by 24
Holzinger, A. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-09891-3_46 · Cited by 25
“Topology as the mathematical study of shapes and spaces that are not rigid [65], pose a lot of possibilities for the application in knowledge discovery and data mining, as topology is the study of connectivity information and it deals with qualitative geometric properties [66].”
BackgroundTopological trajectory clustering with relative persistent homology
Pokorny, F. T., Goldberg, K. Y., & Kragić, D. (2016) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2016.7487092 · Cited by 24
“Besides DC ( X ) , there furthermore exist alternate constructions yielding simplicial complexes, such as the Vietoris-Rips and Witness complexes [8] which are scalable to high-dimensional spaces, but which are not necessarily homotopy equivalent to X r at filtration level r .”
BackgroundBarcodes of Towers and a Streaming Algorithm for Persistent Homology
Kerber, M., & Schreiber, H. (2018) · Discrete & Computational Geometry · doi:10.1007/s00454-018-0030-0 · Cited by 30
“bcomplex within the tower. Experimental evaluations show that our approach can eciently handle towers with billions of complexes. 1 Introduction Motivation and problem statement. Persistent homology [17, 6, 16] is a paradigm to analyze how topological properties of general data sets evolve across multiple scales. Than…”
BackgroundBruno, J. L., Romano, D., Mazaika, P. K., Lightbody, A. A., Hazlett, H. C., Piven, J., & Reiss, A. L. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1620994114 · Cited by 26
“A Reeb graph is a compressed and easily readable visual representation of the data, which preserves its underlying geometric structure and facilitates identification of salient features (14).”
BackgroundFast Estimation of Recombination Rates Using Topological Data Analysis
Humphreys, D. P., McGuirl, M. R., Miyagi, M., & Blumberg, A. J. (2019) · Genetics · doi:10.1534/genetics.118.301565 · Cited by 25
Continuation of point clouds via persistence diagrams
Gameiro, M., Hiraoka, Y., & Obayashi, I. (2015) · Physica D Nonlinear Phenomena · doi:10.1016/j.physd.2015.11.011 · Cited by 25
“, amorphous solids [5, 6], proteins [7], and sensor networks [8] (see also [1] and references therein).”
BackgroundMethodInfluentialYang, W., & Wang, Y. (2024) · EPJ Data Science · doi:10.1140/epjds/s13688-024-00453-6 · Cited by 26
“For details, we refer to these references [73–75].”
MethodTopological Anomaly Detection in Dynamic Multilayer Blockchain Networks
Ofori-Boateng, D., Segovia-Domínguez, I., Akçora, C. G., Kantarcioglu, M., & Gel, Y. R. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-86486-6_48 · Cited by 25
“PH allows to systematically infer qualitative and quantitative multi-lens geometric and topological structures from data directly and, hence, to enhance our understanding on the hidden role of geometry and topology in the system organization [9, 12, 52].”
BackgroundCubical Homology-Based Machine Learning: An Application in Image Classification
Choe, S., & Ramanna, S. (2022) · Axioms · doi:10.3390/axioms11030112 · Cited by 24
The Persistent Homology of Distance Functions under Random Projection
Sheehy, D. R. (2014) · Annual Symposium on Computational Geometry (SoCG) · doi:10.1145/2582112.2582126 · Cited by 25
“What can we say about the topology of a space from just a finite sample, and when can we have any confidence in our answers? These are the questions driving the growing field of topological inference and topological data analysis [11, 10].”
BackgroundExpected sizes of Poisson–Delaunay mosaics and their discrete Morse functions
Edelsbrunner, H., Nikitenko, A., & Reitzner, M. (2017) · Advances in Applied Probability · doi:10.1017/apr.2017.20 · Cited by 24
“Another motivation derives from the work in topological data analysis; see [6].”
BackgroundTumor Segmentation in Whole Slide Images Using Persistent Homology and Deep Convolutional Features
Qaiser, T., Tsang, Y., Epstein, D. B. A., & Rajpoot, N. (2017) · Communications in computer and information science · doi:10.1007/978-3-319-60964-5_28 · Cited by 24
Why Topology for Machine Learning and Knowledge Extraction?
Ferri, M. (2018) · Machine Learning and Knowledge Extraction · doi:10.3390/make1010006 · Cited by 23
“While varying the radius, there may be k-cycles which persist, and a good guess is that those may correspond to the true k-cycles of the sampled object [20,21].”
BackgroundLEARNING FROM SIGNALS DEFINED OVER SIMPLICIAL COMPLEXES
Barbarossa, S., Sardellitti, S., & Ceci, E. (2018) · IEEE Data Science Workshop (DSW) · doi:10.1109/dsw.2018.8439885 · Cited by 24
“[3].”
Feasibility of topological data analysis for event-related fMRI
Ellis, C. T., Lesnick, M., Henselman‐Petrusek, G., Keller, B., & Cohen, J. D. (2019) · Network Neuroscience · doi:10.1162/netn_a_00095 · Cited by 23
“For example, TDA seeks to detect clusters, holes, hollow voids, and tendrils in data (Carlsson, 2009).”
BackgroundMethodIntegrating Systems Modelling and Data Science
Pruyt, E. (2016) · International Journal of System Dynamics Applications · doi:10.4018/ijsda.2016010101 · Cited by 23
On Vietoris–Rips complexes of ellipses
Adamaszek, M., Adams, H., & Reddy, S. (2018) · Journal of Topology and Analysis · doi:10.1142/s1793525319500274 · Cited by 24
“enote either complex. Vietoris{Rips complexes have been used to dene a homology theory for metric spaces [18], in geometric group theory [11], and more recently in applied and computational topology [10, 5]. In applications of topology to data analysis [5], metric space Xis often a nite dataset sampled from an unknown…”
MethodParticles in exhaled air (PExA): non-invasive phenotyping of small airways disease in adult asthma
Soares, M., Mirgorodskaya, E. E., Hatice, K. A., et al. (2018) · Journal of Breath Research · doi:10.1088/1752-7163/aad9d1 · Cited by 23
“The central idea in TDA is that the shape of the data has 253 meaning; by understanding the underlying shape of a data set it is possible to discover 254 interesting features such as clusters or subgroups [34-37].”
BackgroundStrong topology on the set of persistence diagrams
Zarichnyi, M., Savchenko, A., & Kiosak, V. (2019) · AIP conference proceedings · doi:10.1063/1.5130798 · Cited by 23
“ion ofbig data by topological methods. Imposing metric structures in the data set allows for applying techniques from algebraic topology. In this way, the notion of persistent homology was introduced [4]. The persistent homology plays an important role in TDA. The persistence diagrams are used to characterize persiste…”
MethodVietoris–Rips persistent homology, injective metric spaces, and the filling radius
Lim, S., Mémoli, F., & Okutan, O. B. (2024) · Algebraic & Geometric Topology · doi:10.2140/agt.2024.24.1019 · Cited by 51
“and Ghrist and de Silva applied it to sensor networks [14]. Its efficient computation has been addressed by Bauer in [4]. A more detailed historical survery and review of general ideas can be found in [15, 8, 19]. Contributions. One main contribution of this paper is establishing a precise relationship (i.e. a filtered…”
BackgroundExtracting the Main Trend in a Data Set: The Sequencer Algorithm
Baron, D., & Ménard, B. (2021) · The Astrophysical Journal · doi:10.3847/1538-4357/abfc4d · Cited by 23
“Meaningful trends are topological properties of the data, i.e. aspects of the data manifold that are not affected by deformation (Carlsson 2009).”
BackgroundQuantitative analysis of phase transitions in two-dimensional XY models using persistent homology
Giansiracusa, J., & Biagio Lucini, A. (2022) · Cronfa (Swansea University) · Cited by 23
Aspects of topological approaches for data science
Grbić, J., Wu, J., Xia, K., & Wei, G. (2022) · Foundations of Data Science · doi:10.3934/fods.2022002 · Cited by 24
“Other the traditional persistent homology, other TDA models have been proposed for the detailed characterization of the data, such as persistent local homology [18, 63, 16, 19, 5], persistent cohomology [50], multidimensional persistent homology [36, 35, 46, 38], element-specific persistent homology [30, 106], persist…”
BackgroundTemporal Mapper: Transition networks in simulated and real neural dynamics
Zhang, M., Chowdhury, S., & Saggar, M. (2022) · Network Neuroscience · doi:10.1162/netn_a_00301 · Cited by 22
“…data analysis (TDA) tools such as Mapper (Carlsson, 2009; Geniesse et al., 2019, 2022; Saggar et al., 2018, 2022; Singh et al., 2007) and persistent homology (Billings et al., 2021; Carlsson, 2009; Chazal & Michel, 2021; Edelsbrunner & Morozov, 2013; Giusti et al., 2015; Petri et al., 2014).”
MethodInfluentialModeling shear-induced solid-liquid transition of granular materials using persistent homology
Mei, J., Ma, G., Liu, J., et al. (2023) · Journal of the Mechanics and Physics of Solids · doi:10.1016/j.jmps.2023.105307 · Cited by 23
Linear-size approximations to the vietoris-rips filtration
Sheehy, D. R. (2012) · Annual Symposium on Computational Geometry (SoCG) · doi:10.1145/2261250.2261286 · Cited by 27
“See also the survey by Carlsson for background on the topological view of data [3].”
Exploring persistent local homology in topological data analysis
Fasy, B. T., & Wang, B. (2016) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2016.7472915 · Cited by 21
“For more details, see [3, 4] for a gentle introduction to TDA and [5] for algorithmic foundations.”
BackgroundMultiscale Projective Coordinates via Persistent Cohomology of Sparse Filtrations
Perea, J. A. (2017) · Discrete & Computational Geometry · doi:10.1007/s00454-017-9927-2 · Cited by 27
“vided as well as theorems addressing choices in the construction. 1. Introduction Algebraic topology has emerged in the last decade as a powerful framework for analyzing complex high-dimensional data [2,3]. In this setting, a data set is interpreted as a nite subset Xof an ambient metric space (M;d); in practice M is…”
BackgroundInverse Problems in Topological Persistence
Oudot, S., & Solomon, E. (2020) · Abel symposia · doi:10.1007/978-3-030-43408-3_16 · Cited by 24
“yah and McDonald’s book [AM69] is an excellent reference). For an introduction to the themes and tools of topological data analysis, the reader can consult the articles of Ghrist [Ghr08] and Carlsson [Car09]. Lastly, a more formal and comprehensive treatment can be found in the texts by Edelsbrunner and Harer [EH10],…”
MethodSuzuki, A., Miyazawa, M., Okamoto, A., Shimizu, H., Obayashi, I., Hiraoka, Y., Tsuji, T., Kang, P. K., & Ito, T. (2020) · Computers & Geosciences · doi:10.1016/j.cageo.2020.104550 · Cited by 24
Topology Applied to Machine Learning: From Global to Local
Adams, H., & Moy, M. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.668302 · Cited by 25
“In point cloud persistent homology , the input is a finite set of points (a point cloud) residing in Euclidean space or some other metric space (Carlsson, 2009).”
BackgroundQuantitative analysis of phase transitions in two-dimensional XY models using persistent homology
Sale, N., Giansiracusa, J., & Biagio Lucini, A. (2022) · Physical review. E · doi:10.1103/physreve.105.024121 · Cited by 36
Topological origin of phase transitions in the absence of critical points of the energy landscape
Gori, M., Franzosi, R., & Pettini, M. (2018) · Journal of Statistical Mechanics Theory and Experiment · doi:10.1088/1742-5468/aad6b6 · Cited by 23
Hardness results for homology localization
Chen, C., & Freedman, D. (2009) · Institute of Science and Technology Austria · Cited by 19
Comparing feature sets and machine-learning models for prediction of solar flares
Deshmukh, V., Baskar, S., Berger, T. E., Bradley, E., & Meiss, J. D. (2023) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202245742 · Cited by 23
Saranti, A., Pfeifer, B., Gollob, C., Stampfer, K., & Holzinger, A. (2024) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1554 · Cited by 24
“TDA, as also shown in Section 3 is based on the topological representation of the data and tries to construct ML architectures that learn to solve tasks from the properties of this representation (Carlsson, 2009).”
Curvature analysis of frequency modulated manifolds in dimensionality reduction
Guillemard, M., & Iske, A. (2010) · CALCOLO · doi:10.1007/s10092-010-0031-8 · Cited by 22
“In the design of these modern tools, special emphasis is placed on geometrical aspects, where concepts from differential geometry and algebraic topology play an important role [5, 6 , 17, 18, 25, 26].”
BackgroundGromov-Hausdorff Approximation of Filament Structure Using Reeb-type Graph
Chazal, F., & Sun, J. Z. (2014) · Annual Symposium on Computational Geometry (SoCG) · doi:10.1145/2582112.2582129 · Cited by 20
“In the past decade, with the development of topological methods in data analysis, new theories such as topological persistence (see, for example, [22, 38, 9, 10]) and new tools such as the Mapper algorithm [35] have given rise to new algorithms to extract and visualize geometric and topological information from metric…”
MethodProperty Predictions for Dual‐Phase Steels Using Persistent Homology and Machine Learning
Wang, Z., Ogawa, T., & Adachi, Y. (2020) · Advanced Theory and Simulations · doi:10.1002/adts.201900227 · Cited by 22
Li, L., Thompson, C., Henselman‐Petrusek, G., Giusti, C., & Ziegelmeier, L. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.681117 · Cited by 22
“Example Consider the yellow disks in Figure 1 (from [9]) with different numbers of holes and cycle representatives.”
BackgroundInfluentialRaman spectroscopy and topological machine learning for cancer grading
Conti, F., D’Acunto, M., Caudai, C., Colantonio, S., Gaeta, R., Moroni, D., & Pascali, M. A. (2023) · Scientific Reports · doi:10.1038/s41598-023-34457-5 · Cited by 24
“Notwithstanding the precise mathematical definition of these metrics (for which we refer the reader to 20,21 ), an essential property is that the process that associates a PD with data is stable with respect to these metrics.”
BackgroundBhaskar, D., Zhang, W. Y., Volkening, A., Sandstede, B., & Wong, I. Y. (2023) · npj Systems Biology and Applications · doi:10.1038/s41540-023-00302-8 · Cited by 21
“Topological data analysis (TDA) is a promising approach for machine learning of high-dimensional architectures that extracts the “shape” of a dataset based on spatial connectivity [36].”
Topological time-series analysis with delay-variant embedding
Tran, Q. H., & Hasegawa, Y. (2019) · Physical review. E · doi:10.1103/physreve.99.032209 · Cited by 22
Supervised Approaches for Protein Function Prediction by Topological Data Analysis
Martino, A., Rizzi, A., & Mascioli, F. M. F. (2018) · doi:10.1109/ijcnn.2018.8489307 · Cited by 22
“Since patterns are graphs, the set of descriptors will regard centrality measures and Topological Data Analysis (TDA) [22], [23], a novel topology-driven feature extraction approach.”
BackgroundInfluentialPersistent homology of unweighted complex networks via discrete Morse theory
Kannan, H., Saucan, E., Roy, I., & Samal, A. (2019) · Scientific Reports · doi:10.1038/s41598-019-50202-3 · Cited by 22
“[1] Carlsson, G.”
BackgroundInfluentialRandom Čech complexes on Riemannian manifolds
Bobrowski, O., & Oliveira, G. (2018) · Random Structures and Algorithms · doi:10.1002/rsa.20800 · Cited by 21
“ring and statistics. A rising area of research called Topological Data Analysis (TDA), or Applied Topology, focuses on utilizing topology in data analysis, machine learning, and network modeling (see [9, 44, 41] for an introduction). The main idea is to use topological features (e.g. homology, Euler characteristic, pe…”
Background
Metadata
- Authors
- Carlsson, G.
- Affiliations
- Stanford University (US)
- Corresponding author
- Carlsson, Gunnar
- ORCID
- Carlsson, Gunnar
- Venue
- Bulletin of the American Mathematical Society
- Publisher
- American Mathematical Society (AMS)
- Published
- 2009-01-29
- Volume
- 46
- Issue
- 2
- Pages
- 255-308
- ISSN
- 0002-9904 (ISSN-L)
- 1088-9485
- 0273-0979
- OpenAlex
- W1991566301
- Cited by
- 2,302
- References
- 78
- Citations by year
- 2026: 215 · 2025: 216 · 2024: 158 · 2023: 176 · 2022: 159 · 2021: 215 · 2020: 227 · 2019: 151 · 2018: 163 · 2017: 132 · 2016: 128 · 2015: 95 · 2014: 99 · 2013: 65 · 2012: 46
- FWCI
- 33.43
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Bulletin of the American Mathematical Society (publishedVersion)
- Funders
- National Science Foundation (0354543)
- Defense Advanced Research Projects Agency
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Topological and Geometric Data Analysis
- Physical Sciences › Mathematics › Geometry and Topology › Geometric and Algebraic Topology
- Physical Sciences › Mathematics › Applied Mathematics › Geometric Analysis and Curvature Flows
- Keywords
- point clouds, distance measures, high-dimensional data, biological data, microarray data, high-throughput data
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