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 601–700 of 2,331
Topological Data Analysis of Single-Cell Hi-C Contact Maps
Carrière, M., & Rabadán, R. (2020) · Abel symposia · doi:10.1007/978-3-030-43408-3_6 · Cited by 18
Marcone, R., Carrière, M., Rougemont, J., Oudot, S., Hess, K. P., & Brisken, C. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz052 · Cited by 16
A topology-based descriptor for 3D point cloud modeling: Theory and experiments
Beksi, W. J., & Papanikolopoulos, N. P. (2019) · Image and Vision Computing · doi:10.1016/j.imavis.2019.05.004 · Cited by 16
Replays of spatial memories suppress topological fluctuations in cognitive map
Babichev, A., Morozov, D., & Dabaghian, Y. A. (2018) · Network Neuroscience · doi:10.1162/netn_a_00076 · Cited by 17
“…Petri et al., 2014) studies, and allows using a powerful Network Neuroscience arsenal of methods from algebraic topology, in particular persistent (Carlsson, 2009; Lum et al., 2013; Singh et al., 2008) and zigzag (Carlsson & De Silva, 2010; Carlsson, De Silva, & Morozov 2009) homology theory…”
Topology and curvature of metric spaces
Joharinad, P., & Jost, J. (2019) · Advances in Mathematics · doi:10.1016/j.aim.2019.106813 · Cited by 17
“terns of distance balls and therefore is meaningful even for discrete metric spaces. Such intersection patterns have already been investigated from different perspectives. In topological data analysis [8], the concept of persistent homology records how such intersection patterns change when the radii of those distance…”
BackgroundA framework for second-order eigenvector centralities and clustering coefficients
Arrigo, F., Higham, D. J., & Tudisco, F. (2020) · Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rspa.2019.0724 · Cited by 17
“So, efficiently accounting for higher-order topology can allow more robust and effective quantification of nodal importance in various senses [9, 43].”
BackgroundData-Driven Classification of Coronal Hole and Streamer Belt Solar Wind
Bloch, T., Watt, C. E. J., Owens, M. J., McInnes, L., & Macneil, A. (2020) · Solar Physics · doi:10.1007/s11207-020-01609-z · Cited by 18
“The field of topological data analysis (Carlsson, 2009) uses methods from topology to better understand complex datasets.”
MethodClustering of local extrema in Planck CMB maps
Sadr, A. V., & Movahed, M. S. (2021) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stab368 · Cited by 18
Persistent Cohomology for Data With Multicomponent Heterogeneous Information
Cang, Z., & Wei, G. (2020) · SIAM Journal on Mathematics of Data Science · doi:10.1137/19m1272226 · Cited by 27
Numeric invariants from multidimensional persistence
Skryzalin, J., & Carlsson, G. (2017) · Journal of Applied and Computational Topology · doi:10.1007/s41468-017-0003-z · Cited by 16
“An algorithm to calculate the rank invariant is given in Carlsson et al. (2010). For a 2 Nnþ, let I 1⁄4 fi j ai 1⁄4 1g, and let J 1⁄4 fi j ai [ 1g.”
Self-citationSethares, W. A., & Budney, R. (2014) · Journal of Mathematics and Music · doi:10.1080/17459737.2013.850597 · Cited by 18
“Introduced by Carlsson (2009) as a way of parsing large data sets, persistent homology takes as its starting point the functoriality of homology.”
BackgroundInfluentialMurthy, K. N. K., Leary, O. P., Merck, L. H., et al. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2254227 · Cited by 17
Fractal dimension and the persistent homology of random geometric complexes
Schweinhart, B. (2020) · Advances in Mathematics · doi:10.1016/j.aim.2020.107291 · Cited by 20
“, [14, 15, 30, 31, 33].”
Limit Theorems in Discrete Stochastic Geometry
Yukich, J. E. (2012) · Lecture notes in mathematics · doi:10.1007/978-3-642-33305-7_8 · Cited by 17
“The Vietoris-Rips complex is studied in connection with the statistical analysis of high-dimensional data sets [15], manifold reconstruction [20], and it has also received attention amongst topologists in connection with clustering and connectivity questions of data sets [14].”
BackgroundTopological signal processing and learning: Recent advances and future challenges
Isufi, E., Leus, G. J. T., Beferull‐Lozano, B., Barbarossa, S., & Lorenzo, P. D. (2025) · Signal Processing · doi:10.1016/j.sigpro.2025.109930 · Cited by 20
Gardini, E., Ferrarotti, M. J., Cavalli, A., & Decherchi, S. (2021) · Cognitive Computation · doi:10.1007/s12559-021-09823-y · Cited by 17
Learning Topology: Bridging Computational Topology and Machine Learning
Moroni, D., & Pascali, M. A. (2021) · Pattern Recognition and Image Analysis · doi:10.1134/s1054661821030184 · Cited by 16
Smart Contract Scams Detection with Topological Data Analysis on Account Interaction
Fan, S., Fu, S., Luo, Y., Xu, H., Zhang, X., & Xu, M. (2022) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3511808.3557454 · Cited by 16
“IC𝑣𝑡 ,OC 𝑣 𝑡 ,DC 𝑣 𝑡 ,AND 𝑣 𝑡 , PR 𝑣 𝑡 , SCC 𝑣 𝑡 , Ecc 𝑣 𝑡 , SP 𝑣 𝑡 ) 7 if 𝑣 has direct interaction with 𝑥 then 8 𝑋𝑥+ = 𝑋 𝑣𝑡 9 return 𝑋 Algorithm 2: TDA-based Contract Prediction Input: Discrete dynamic interaction graph G = {G1,G2, ...,G𝑡 }, set of contract accounts X2, lens function 𝑓 .”
BackgroundInfluentialProbing center vortices and deconfinement in SU(2) lattice gauge theory with persistent homology
Sale, N., Biagio Lucini, A., & Giansiracusa, J. (2023) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.107.034501 · Cited by 16
“Persistent homology is a computational topology tool introduced in its modern form in [43] and popularised in [68].”
MethodAssessing nocturnal scratch with actigraphy in atopic dermatitis patients
Ji, J., Venderley, J., Zhang, H., Lei, M., Ruan, G., Patel, N., Chung, Y.-M., Giesting, R., & Miller, L. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00821-y · Cited by 19
Curiosity as filling, compressing, and reconfiguring knowledge networks
Patankar, S. P., Zhou, D., Lynn, C. W., Kim, J. Z., Ouellet, M., Ju, H., Zurn, P., Lydon‐Staley, D. M., & Bassett, D. S. (2023) · Collective Intelligence · doi:10.1177/26339137231207633 · Cited by 20
“Bianconi, 2021; Carlsson, 2009; Ghrist, 2007; Hatcher, 2002; Sizemore et al., 2019; Zomorodian and Carlsson, 2005.”
Govc, D., & Škraba, P. (2017) · Foundations of Computational Mathematics · doi:10.1007/s10208-017-9368-6 · Cited by 16
“A powerful tool in TDA is persistent homology, which studies the homology of a filtration rather than a single space.”
BackgroundCanonical tensor model through data analysis: Dimensions, topologies, and geometries
Kawano, T., Obster, D., & Sasakura, N. (2018) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.97.124061 · Cited by 14
“ighborhoods. This characterizes local topological structure of fuzzy spaces. Global topological structure is also of much interest. In this section, we introduce the notion called persistent homology [28] as a method to extract the homological structure of the fuzzy spaces. Let us rst assume that a distance d(;) betwe…”
MethodInfluentialHomological connectivity in random Čech complexes
Bobrowski, O. (2022) · Probability Theory and Related Fields · doi:10.1007/s00440-022-01149-6 · Cited by 17
“[12, 20]).”
Chatter Diagnosis in Milling Using Supervised Learning and Topological Features Vector
Yesilli, M. C., Tymochko, S., Khasawneh, F. A., et al. (2019) · IEEE International Conference on Machine Learning and Applications (ICMLA) · doi:10.1109/icmla.2019.00200 · Cited by 16
“This section briefly describes the main concepts of 1-D persistent homology, but we defer a more thorough treatment to references in the literature such as [30–34].”
BackgroundInferring symbolic dynamics of chaotic flows from persistence
Yalnız, G., & Budanur, N. B. (2020) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.5122969 · Cited by 16
Measuring human activity spaces from GPS data with density ranking and summary curves
Chen, Y., & Dobra, A. (2020) · The Annals of Applied Statistics · doi:10.1214/19-aoas1311 · Cited by 20
“These curves fall within the broader domain of topological data analysis which is a flexible framework for detecting the structure and creating lower-dimensional summaries of distributions of complex or highdimensional datasets (Kaczynski et al., 2004; Edelsbrunner and Harer, 2008, 2010; Carlsson, 2009; Lum et al., 20…”
BackgroundFrom boundaries to bumps: When closed (extremal) contours are critical
Kunsberg, B., & Zucker, S. W. (2021) · Journal of Vision · doi:10.1167/jov.21.13.7 · Cited by 16
“But when noise and sampling are introduced, they introduce technical problems in assessing topological signatures: since pixels are discrete, what is the precise location of a singularity; since changes can only occur in the x- and ydirections (image coordinates), how can the actual gradient be specified; is this a re…”
BackgroundNonparametric Anomaly Detection on Time Series of Graphs
Ofori-Boateng, D., Gel, Y. R., & Cribben, I. (2020) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2020.1844214 · Cited by 15
Topological Machine Learning Methods for Power System Responses to Contingencies
Bush, B., Chen, Y., Ofori-Boateng, D., et al. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i17.17791 · Cited by 16
“From standard literature, the Vietoris-Rips (VR) combinatorial complex is the most popular complex choice in TDA due to its simplicity and computational benefits (Carlsson 2009; Zomorodian 2010).”
BackgroundUnsupervised learning of topological phase diagram using topological data analysis
Park, S., Hwang, Y., & Yang, B. (2022) · Physical review. B./Physical review. B · doi:10.1103/physrevb.105.195115 · Cited by 16
“[11] Gunnar Carlsson, “Topology and data,” Bulletin of the American Mathematical Society 46, 255–308 (2009).”
BackgroundTopological theory of phase transitions
Gori, M., Franzosi, R., Pettini, G., et al. (2022) · Journal of Physics A Mathematical and Theoretical · doi:10.1088/1751-8121/ac7f09 · Cited by 16
Exact Topology of the Dynamic Probability Surface of an Activated Process by Persistent Homology
Manuchehrfar, F., Li, H., Tian, W., Ma, A., & Liang, J. (2021) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.1c00904 · Cited by 16
“Persistent homology computes these holes and measures their scales at different spatial resolutions [48, 49].”
Local Cohomology and Stratification
Nanda, V. (2019) · Foundations of Computational Mathematics · doi:10.1007/s10208-019-09424-0 · Cited by 16
“There now exists a substantial body of work where filtered (co)homology groups of cell complexes built around data points play a central role [3], [12], [8].”
BackgroundTopological Data Analysis of Financial Time Series: Landscapes of CrashesPreprint
Gidea, M., & Katz, Y. A. (2017) · SSRN Electronic Journal · doi:10.2139/ssrn.2931836 · Cited by 17
The topology and geometry of neural representations
Lin, B., & Kriegeskorte, N. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2317881121 · Cited by 25
“, persistent homology [56] and TDA mapping [57]).”
BackgroundEdwards, P. B., Skruber, K., Milićević, N., Heidings, J. B., Read, T., Bubenik, P., & Vitriol, E. A. (2021) · Patterns · doi:10.1016/j.patter.2021.100367 · Cited by 18
Landsifier v1.0: a Python library to estimate likely triggers of mapped landslides
Rana, K., Malik, N., & Öztürk, U. (2022) · Natural hazards and earth system sciences · doi:10.5194/nhess-22-3751-2022 · Cited by 15
Souto, H. G., & Moradi, A. (2023) · Decision Analytics Journal · doi:10.1016/j.dajour.2023.100369 · Cited by 15
Emmett, K., & Rabadán, R. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-09891-3_49 · Cited by 15
“We provide a brief overview of TDA and persistent homology in Section 2, for additional reviews see [2][7].”
The Normalized Graph Cut and Cheeger Constant: From Discrete to Continuous
Ery Arias-castro, A., Bruno Pelletier, B., & Pudlo, P. (2012) · Advances in Applied Probability · doi:10.1239/aap/1354716583 · Cited by 26
“the number of connected components [5], the intrinsic dimensionality [29] and, more generally, the homology [10], [11], [13], [14], [34], [37], [44]; the Minkowski content [17], as well as the perimeter and area (volume) [8].”
BackgroundPersistent homology and string vacua
Cirafici, M. (2016) · Journal of High Energy Physics · doi:10.1007/jhep03(2016)045 · Cited by 20
“Again we refer the reader to [3, 4, 5] for more examples of the practical uses of these techniques.”
BackgroundMethodRamachandran, R. K., Wilson, S., & Berman, S. (2017) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2016.2647641 · Cited by 15
“We employ tools from topological data analysis (TDA) to segment the obstacle regions in the domain by constructing a probability-based filtration on the domain’s free space, thereby simultaneously computing the optimal filtration parameter and estimating the number of topological features in the domain.”
InfluentialData Analysis from Empirical Moments and the Christoffel Function
Pauwels, E., Putinar, M., & Lasserre, J. B. (2020) · Foundations of Computational Mathematics · doi:10.1007/s10208-020-09451-2 · Cited by 15
Reorderability of node-filtered order complexes
Sizemore, A. E., & Bassett, D. S. (2020) · Physical review. E · doi:10.1103/physreve.101.052311 · Cited by 14
“erty that the two would share is a similar evolving global topology. Quantitatively tracking and characterizing that evolving global topology is made tractable by emerging tools from applied topology [10, 11, 83], specically, persistent homology which records the pattern of topological cavity emergence throughout netw…”
BackgroundMethodPersistence modules, symplectic Banach–Mazur distance and Riemannian metrics
Stojisavljević, V., & Zhang, J. (2021) · International Journal of Mathematics · doi:10.1142/s0129167x21500403 · Cited by 15
“For a detailed exposition see [22, 27, 14, 63, 11, 21, 41, 15, 45].”
Topological Data Analysis of Nanoscale Roughness in Brass Samples
Zhukov, M. V., Hasan, M. S., Nesterov, P. V., Sabbouh, M., Burdulenko, O., Скорб, Е. В., & Nosonovsky, M. (2021) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.1c20694 · Cited by 15
Choi, Y., Park, J., Hwang, D., Kook, W., Kim, Y.-J., Tanaka, H., Hozumi, T., Yuasa, T., Ling, L. H., Yu, C., et al. (2022) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2021.12.014 · Cited by 15
Generalized persistence algorithm for decomposing multiparameter persistence modules
Dey, T. K., & Xin, C. (2022) · Journal of Applied and Computational Topology · doi:10.1007/s41468-022-00087-5 · Cited by 24
“The persistence algorithm [29] and its algebraic formulation afterward [52] seeded the area called topological data analysis (TDA) that is addressing various problems in data science [10, 13, 22, 31, 43, 44, 47, 48, 49, 51].”
BackgroundGuo, G., Zhao, Y., Liu, C., Fu, Y., Xi, X., Jin, L., Shi, D., Wang, L., Duan, Y., Huang, J., et al. (2022) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2022.1024205 · Cited by 16
“R ( X , ε ) is a Vietoris–Rips (VIPs) complex (Carlsson, 2009)”
BackgroundReconstructing persistent graph structures from noisy images
Chernov, A., & Kurlin, V. A. (2013) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · Cited by 15
Zhang, Z., Yang, S., Cui, H., et al. (2015) · doi:10.1109/embc.2015.7318419 · Cited by 15
Topic Detection in Twitter Using Topology Data Analysis
Torres-Tramón, P., Hromic, H., & Heravi, B. R. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24800-4_16 · Cited by 15
Lesnick, M., Rabadán, R., & Rosenbloom, D. I. S. (2020) · SIAM Journal on Applied Algebra and Geometry · doi:10.1137/18m118150x · Cited by 15
“Our treatment of persistent homology will be terse; for a more thorough introduction to these ideas, including a discussion of some of the many applications of persistent homology to data analysis, see the surveys and textbooks [10, 11, 22, 42].”
BackgroundMeasures of order for nearly hexagonal lattices
Motta, F. C., Neville, R., Shipman, P. D., et al. (2018) · Physica D Nonlinear Phenomena · doi:10.1016/j.physd.2018.05.005 · Cited by 15
Lupo, U., Medina-Mardones, A. M., & Tauzin, G. (2022) · Journal of Applied and Computational Topology · doi:10.1007/s41468-022-00093-7 · Cited by 15
Ensemble forecasting of the Zika space‐time spread with topological data analysis
Soliman, M., Lyubchich, V., & Gel, Y. R. (2020) · Environmetrics · doi:10.1002/env.2629 · Cited by 14
“l topology, statistics, and machine learning which allows for a systematic multi-lens assessment of the underlying topology and geometry of the data generating process (Zomorodian and Carlsson, 2005; Carlsson, 2009; Chazal and Michel, 2017). In this paper, we primarily employ tools of persistent homology (PH) within t…”
MethodSegovia-Domínguez, I., Zhen, Z., Wagh, R., et al. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-75762-5_17 · Cited by 15
Xia, K., & Wei, G. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.01735 · Cited by 22
“2 Persistent homology As a branch of algebraic topology, persistent homology is a topological approach that utilizes algebraic algorithms to compute topological invariants in data.(44,185,211,237,279) It is a working horse in the popular topological data analysis and has found its success in qualitative characterizati…”
BackgroundMethodInfluentialRazmarashooli, A., Chua, Y. K., Barzegar, V., Salazar, D., Laflamme, S., Hu, C. K., Downey, A. R. J., Dodson, J. C., & Schrader, P. T. (2024) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2024.112048 · Cited by 14
Unsupervised machine learning approaches to the q-state Potts model
Tirelli, A., Carvalho, D. O., Oliveira, L. A., de Lima, J. P., Costa, N. C., & dos Santos, R. R. (2022) · The European Physical Journal B · doi:10.1140/epjb/s10051-022-00453-3 · Cited by 18
Persistent Homology for Breast Tumor Classification Using Mammogram Scans
Asaad, A., Ali, D., Majeed, T. F., & Rashid, R. D. (2022) · Mathematics · doi:10.3390/math10214039 · Cited by 17
“Persistent Homology (PH), the main tool of TDA, extracts topological summaries from data in the form of connected components, loops and cavities using a process known as filtration which relies on a nested sequence of simplicial complexes that capture birth and death of those topological invariants [1].”
BackgroundA weighted network clustering approach in the NBA
Muniz, M., & Flamand, T. (2022) · Journal of Sports Analytics · doi:10.3233/jsa-220584 · Cited by 14
“Alagappan (2012) builds a sem-blance of a network structure to form player clusters, though topological data analysis is more precisely a geometric approach (Carlsson 2009).”
Chulián, S., Stolz, B. J., Martínez-Rubio, Á., Goñi, C. B., Gutiérrez, J. F. R., Caballero‐Velázquez, T., Quintana, Á. M., Ramírez, M. R., Robleda, A. C., Soler, J. L. F., et al. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011329 · Cited by 14
“Topological data analysis (TDA) [22, 23] is a set of methods that uses ideas from the mathematical discipline topology to study the ‘shape’ of high-dimensional data.”
MethodSinger, B., Meling, D., Hirsch‐Hoffmann, M., Michels, L., Kometer, M., Smigielski, L., Dornbierer, D., Seifritz, E., Vollenweider, F. X., & Scheidegger, M. (2024) · Scientific Reports · doi:10.1038/s41598-024-55726-x · Cited by 20
Kulkarni, S., Pharasi, H. K., Vijayaraghavan, S., Kumar, S., Chakraborti, A., & Samal, A. (2024) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2024.129785 · Cited by 18
“For more details on persistent homology, see e.g. [52, 116, 117].”
BackgroundTopological Data Analysis for Robust Gait Biometrics Based on Wearable Sensors
Liu, Y.-S., Ivanov, K., Wang, J., et al. (2024) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2024.3396177 · Cited by 14
“Persistent homology [41], [42], a key TDA tool, has been proposed by Carlsson [43], validating the ability for structural analysis of data points.”
MethodClique Homology is $${{\mathsf{QMA}}}_{1}$$-hard
Crichigno, M., & Kohler, T. (2024) · Nature Communications · doi:10.1038/s41467-024-54118-z · Cited by 15
“Indeed, simplicial complexes are the main objects studied in computational topology [1, 2] and play a fundamental role in various areas of pure and applied mathematics and including data analysis [3].”
Topological Eulerian Synthesis of Slow Motion Periodic Videos
Tralie, C. J., & Berger, M. (2018) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2018.8451014 · Cited by 14
“Our geometric framework also allows us to use topological data analysis (TDA) [14, 15, 16, 17, 18] and fundamental frequency estimation [19] to autotune the spatial and temporal scales of the periodic motion, respectively, so that no user intervention is required.”
MethodInfluentialTopological data analysis for true step detection in periodic piecewise constant signals
Khasawneh, F. A., & Munch, E. (2018) · Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rspa.2018.0027 · Cited by 23
“ve mathematical guarantees for the resulting change detection which enables accurate identication and counting of the true pulses. The approach we utilize is based on Topological Data Analysis (TDA) [15,16,17,18,19,20] and it is outlined in Section3. The prevalence of Topological Data Analysis (TDA) has exploded in re…”
MethodInfluentialMarcolli, M. (2019) · Journal of Geometry and Physics · doi:10.1016/j.geomphys.2019.02.004 · Cited by 14
“• Statistics : data analysis has incorporated topological methods in the form of persistent topology and persistent homology, which are based on simplicial sets associated to a collection of data points, [6].”
BackgroundGauging Functional Brain Activity: From Distinguishability to Accessibility
Papo, D. (2019) · Frontiers in Physiology · doi:10.3389/fphys.2019.00509 · Cited by 14
“…multiple observables, observation scales and Frontiers in Physiology | www.frontiersin.org 5 May 2019 | Volume 10 | Article 509 geometries, and defining relationships between geometric objects constructed using different parameter values and continuous maps relating these objects (Carlsson, 2009).”
BackgroundWhite Matter Brain Network Research in Alzheimer’s Disease Using Persistent Features
Kuang, L., Gao, Y. X., Chen, Z., Xing, J., Xiong, F., & Xie, H. (2020) · Molecules · doi:10.3390/molecules25112472 · Cited by 17
“More persistent features are detected over a wide range of length and are deemed more likely to represent true features of the underlying space, rather than artifacts of sampling, noise, or particular choice of parameters [53].”
BackgroundFrom Trees to Barcodes and Back Again: Theoretical and Statistical Perspectives
Kanari, L., Garin, A., & Hess, K. P. (2020) · Algorithms · doi:10.3390/a13120335 · Cited by 14
“ough geometric approaches to analyzing data have been extensively used for many years, the rst topological methods for data analysis were developed only recently, e.g., [8], [7], [22], [26], [25] and [4]. Topological Data Analysis (TDA) is a fairly new eld at the intersection of data science and algebraic topology, th…”
MethodCritical and Ictal Phases in Simulated EEG Signals on a Small-World Network
Nemzer, L. R., Cravens, G. D., Worth, R. M., Motta, F. C., Placzek, A. N., Castro, V., & Lou, J. Q. (2021) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2020.583350 · Cited by 15
“Then TDA methods can be applied to the data set of system weights to gain insights into how the DLNN system is making decisions.”
BackgroundFunctional limit theorems for the euler characteristic process in the critical regime
Thomas, A. M., & Owada, T. (2021) · Advances in Applied Probability · doi:10.1017/apr.2020.46 · Cited by 14
“isticofsublevel(orsuperlevel)setsofrandomfieldshavefound broad applications [1, 8]. In TDA, the technique of capturing the dynamic evolution of topology is generally studied in persistent homology—see [7] for a good introduction. Persistenthomologyoriginatedincomputationaltopology[10]andhasreceivedmuchattention as a us…”
MethodRelationship between local coordinates and thermal conductivity in amorphous carbon
Minamitani, E., Shiga, T., Kashiwagi, M., et al. (2022) · Journal of Vacuum Science & Technology A Vacuum Surfaces and Films · doi:10.1116/6.0001744 · Cited by 14
Topological analysis of brain dynamics in autism based on graph and persistent homology
Jafadideh, A. T., & Asl, B. M. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106202 · Cited by 14
Symmetric and r-symmetric tropical polynomials and rational functions
Carlsson, G., & Kališnik, S. (2016) · Journal of Pure and Applied Algebra · doi:10.1016/j.jpaa.2016.05.002 · Cited by 15
“ot finitely generated, but the elementary r-symmetric polynomials do generate r-symmetric rational functions. Our motivation for the study of this problem came out of the study of persistence barcodes [4],[3],[5]. The interpretation of the space of persistence barcodes as embedded in the geometric points of an affine sch…”
BackgroundSelf-citationErden, F., & Çetin, A. E. (2017) · IEEE Signal Processing Letters · doi:10.1109/lsp.2017.2699924 · Cited by 14
“When all the complete simplices are filled in similarly, we obtain the Vietoris–Rips complex [21].”
BackgroundMethodCluster Analysis of Haze Episodes Based on Topological Features
Zulkepli, N. F. S., Noorani, M. S. M., Razak, F. A., et al. (2020) · Sustainability · doi:10.3390/su12103985 · Cited by 14
Behavior of compressed plasmas in magnetic fields
Ganguli, G., Crabtree, C., Fletcher, A. C., & Amatucci, B. (2020) · Reviews of Modern Plasma Physics · doi:10.1007/s41614-020-00048-4 · Cited by 14
“Further advancements in discovery of stable and accurate hybrid solutions in complex systems modeling can be achieved by leveraging methods from computational topology which showed promising results in a wide range of applications (Carlsson 2009; Edelsbrunner 2014; Garland et al.”
BackgroundHomological Percolation: The Formation of Giant k-Cycles
Bobrowski, O., & Škraba, P. (2020) · International Mathematics Research Notices · doi:10.1093/imrn/rnaa305 · Cited by 14
“The field of applied topology (or topological data analysis) promotes the use of mathematical topology in data and network analysis [12, 18, 20] One of the most powerful tools developed in this field is persistent homology [17, 39].”
BackgroundTape surfaces characterization with persistence images
Frahi, T., Argerich, C., Yun, M., et al. (2020) · AIMS Materials Science · doi:10.3934/matersci.2020.4.364 · Cited by 14
Homology Computation of Large Point Clouds using Quantum AnnealingPreprint
Dridi, R., & Alghassi, H. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1512.09328 · Cited by 16
“y other means. An excellent survey of TDA and its applications, in addition to a gentle introduction to the notions of homotopy equivalence, simplicial complexes and their homologies, can be found in [Car09] and [Zom12]; also we refer to [Mas91] and [Hat02] for more advanced reading. The objective of the paper is to p…”
Higher-order connection Laplacians for directed simplicial complexes
Gong, X., Higham, D. J., Zygalakis, K. C., & Bianconi, G. (2024) · Journal of Physics Complexity · doi:10.1088/2632-072x/ad353b · Cited by 12
“Simplicial complexes are higher-order networks that are amenable to a comprehensive higher-order topological treatment revealing the topology of data [13–18] leading to applications in neuroscience [19–21], biology [22–25], sensor networks [26, 27], and computer graphics [28, 29].”
BackgroundAtt-Next for skin lesion segmentation with topological awareness
Katar, C., Eryilmaz, O. B., & Ekşioğlu, E. M. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.127637 · Cited by 15
Denoising: a powerful building block for imaging, inverse problems and machine learning
Milanfar, P., & Delbracio, M. (2025) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2024.0326 · Cited by 37
“It has been observed [7, 30, 31] that such denoisers behave (at least locally) as shrinkage operations in an orthonormal basis adapted to the underlying structure of the image.”
Structural Analysis of Multivariate Point Clouds Using Simplicial Chains
Rieck, B., & Leitte, H. (2014) · Computer Graphics Forum · doi:10.1111/cgf.12398 · Cited by 12
“Because topological methods depend on intrinsic properties of a point cloud, they are less sensitive to the choice of metrics [Car09].”
BackgroundA Novel Framework for Pulse Pressure Wave Analysis Using Persistent Homology
Emrani, S., Saponas, T. S., Morris, D., et al. (2015) · IEEE Signal Processing Letters · doi:10.1109/lsp.2015.2441068 · Cited by 13
“We propose a new framework for extracting the characteristic points of the peripheral pressure wave based on persistent homology [8], and we test this framework on real data.”
MethodOudot, S., & Sheehy, D. R. (2013) · Annual Symposium on Computational Geometry (SoCG) · doi:10.1145/2462356.2462371 · Cited by 15
“See also [4] for a more general and high-level introduction to the topological analysis of point cloud data.”
K−means clustering on the space of persistence diagrams
Maroulas, V., Mike, J. L., & Marchese, A. (2017) · Wavelets and Sparsity · doi:10.1117/12.2273067 · Cited by 13
Detecting intermediate protein conformations using algebraic topology
Haspel, N., Luo, D., & González, E. (2017) · BMC Bioinformatics · doi:10.1186/s12859-017-1918-z · Cited by 13
“We extract information of X from Z , using Persistent Homology [26] to estimate the topology of X through algorithms applied to the approximation Z .”
MethodCha, B., Park, S., Kim, J., et al. (2018) · Security and Communication Networks · doi:10.1155/2018/1746809 · Cited by 13
Financial ratios and stock returns reappraised through a topological data analysis lens
Dłotko, P., Qiu, W., & Rudkin, S. (2021) · European Journal of Finance · doi:10.1080/1351847x.2021.2009892 · Cited by 13
“2See Carriere and Oudot (2018) for a further review of the original mapper software.”
BackgroundOn approximation theorems for the Euler characteristic with applications to the bootstrap
Krebs, J., Roycraft, B., & Polonik, W. (2021) · Electronic Journal of Statistics · doi:10.1214/21-ejs1898 · Cited by 13
“and Carlsson (2009). The presented central limit theorems rely heavily on the approach of Penrose and Yukich (2001), who define and use the idea of stabilizing functionals (Lee (1997, 1999)). The approach of Penrose and Yukich has also been used to establish central limit theorems for Betti numbers and persistent Bett…”
BackgroundInfluentialFat regulatory mechanisms of pine nut oil based on protein interaction network analysis
Jin, S. C., Kim, M. H., Choi, L. Y., et al. (2021) · Phytomedicine · doi:10.1016/j.phymed.2021.153557 · Cited by 13
The Euler characteristic and topological phase transitions in complex systems
Filho, E. C. D. A., Moreira, R. A., & Santos, F. A. N. (2022) · Journal of Physics Complexity · doi:10.1088/2632-072x/ac664c · Cited by 13
“In this context, topological data analysis (TDA) emerges as a promising methodolgical tool [1].”
BackgroundInfluentialIsmail, M. S., Ismail, M. S., Noorani, M. S. M., et al. (2022) · Frontiers in Applied Mathematics and Statistics · doi:10.3389/fams.2022.940133 · Cited by 13
Enhanced Approach for Agglomerative Clustering Using Topological Relations
Alomari, H. W., Al-Badarneh, A. F., Al-Alaj, A., & Khamaiseh, S. Y. (2023) · IEEE Access · doi:10.1109/access.2023.3252374 · Cited by 13
“This is demonstrated by how topological and geometrical methods are leveraged to analyze intricate data and create abstract summaries or representations of all the data features, which enables the identification of relationships and patterns in data at a faster rate [2], [29], [30].”
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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