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.
plainstates a cause- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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- 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)
hedgedstates an association- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
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- 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.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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- 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)
plainstates a cause- Asserted: passes — The authors put it forward as true.
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- 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)
plainstates an association- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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- 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.
plainstates an association- 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.
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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)
plainstates a cause- 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.
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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)
plainstates an association- 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.
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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
boostedstates an association- 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.
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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.
hedgedstates an associationSingle 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
hedgedstates an associationSingle 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
hedgedstates a cause- 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
plainstates a cause- 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
boostedstates an association- 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.
hedgedstates an association- 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
hedgedstates an association- 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.
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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
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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
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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
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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
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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
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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
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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
boostedstates a cause- Asserted: passes — The authors put it forward as true.
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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.
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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.
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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.
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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.
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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
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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.
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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
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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
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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
plainstates a cause- Asserted: passes — The authors put it forward as true.
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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
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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.
plainstates a cause- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
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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
hedgedstates a cause- Asserted: passes — The authors put it forward as true.
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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
hedgedstates an association- Asserted: passes — The authors put it forward as true.
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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.
plainstates an association- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
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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.
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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.
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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.
plainstates a cause- Asserted: passes — The authors put it forward as true.
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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.
plainstates an association- Asserted: passes — The authors put it forward as true.
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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.
boostedstates a cause- Asserted: passes — The authors put it forward as true.
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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.
plainstates a cause- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
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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.
hedgedstates a cause- Asserted: passes — The authors put it forward as true.
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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
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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
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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
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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.
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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.
plainstates an association- Asserted: passes — The authors put it forward as true.
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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
plainstates an association- Asserted: passes — The authors put it forward as true.
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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.
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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.
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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.
plainstates a cause- Asserted: passes — The authors put it forward as true.
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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.
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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.
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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.
hedgedstates a cause- Asserted: passes — The authors put it forward as true.
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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
plainstates a cause- 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.
plainstates a cause- 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
plainstates an association- 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
plainstates an association- 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
boostedstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
None of the sources cited for this claim have been checked yet.
In the paper
“Each such interval can be considered as a point in the set D = {(x, y) ∈ R 2 |x ≤ y}, and the output is therefore a finite subset of the set D. As such, we now have a finite spatial point process (see [20], [3]).”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Additional information
What the sources say
- Not matchedcites [20] Daley, D. J., 1988
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked — no full text of the sourcecites [3] Baddeley, Adrian, 2007, Spatial point processes and their applications source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
The paper relies on this without support.
There is no classification theorem for R-persistence abelian groups.
“However, we do not have such a theorem.”
The paper relies on this without support.
There is still no classification theorem for graded Z[t]-modules.
“Now, there is still no classification theorem for graded Z[t]-modules.”
The paper relies on this without support.
In studying data from experiments, one typically cannot assume that the data lies along a submanifold.
“If one is interested in studying data from experiments, though, one typically cannot assume that the data lies along a submanifold.”
The paper relies on this without support.
Even if one could assume that the data lies along a manifold, one is usually not in a position to verify that the stringent hypotheses of [52] are satisfied.
“Further, even if one could assume that the data lies along a manifold, one is usually not in a position to verify that the stringent hypotheses of [52] are satisfied.”
The paper relies on this without support.
The analysis of populations of neurons is amenable to geometric analysis.
“The second problem appears to be very amenable to geometric analysis, since it will involve the activities of several neurons at once.”
The paper relies on this without support.
The mechanism by which V1 carries out low-level tasks such as edge and line detection is not understood.
“However, the mechanism by which it carries out these tasks is not understood.”
The paper relies on this without support.
The classification of finitely generated modules over multivariable polynomial rings is much more complicated than the corresponding result for single-variable polynomial rings, and no reasonable parametrization is known.
“As is well known to algebraists, the classification of finitely generated modules over multivariable polynomial rings is much more complicated than the corresponding result for single variable polynomial rings, and in fact no reasonable parametrization is known.”
The paper relies on this without support.
In the case of more than one variable, the classification of multigraded modules depends on the field in question.
“examples show that in the case of more than one variable, the classification of multigraded modules definitely depends on the field in question.”
The paper relies on this without support.
The classification in the multivariable case is parametrized by points in moduli varieties over the ground field.
“In fact, the classification in the multivariable case is parametrized by points in moduli varieties over the ground field”
The paper relies on this without support.
Hierarchical clustering algorithms do not report single partitions but rather dendrograms, which are roughly speaking R+-persistent sets.
“This is not an unreasonable thing to do in view of the fact that hierarchical clustering algorithms do not report single partitions but rather dendrograms, which are roughly speaking R + -persistent sets.”
Citations
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“We note that the correspondence X → π0(X) can actually be viewed as a functor (see [44]) from the category of topological spaces to the category of sets, in the sense that a continuous map f : X → Y induces a map of sets π0(f) : π0(X)→ π0(Y ), satisfying certain obvious conditions on composite maps and identity maps.”
BackgroundMethodDensity Estimation for Statistics and Data Analysis
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Density Estimation for Statistics and Data Analysis
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Research works printed but not linked: 4
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- Dummit, David S., 2004, Abstract algebra
- Huber, Peter J., 1985, Projection pursuit, Ann. Statist., 13, 435, 10.1214/aos/1176349519 · DOI · Paper page
- Reeb, Georges, 1946, Sur les points singuliers d’une forme de Pfaff complètement intégrable ou d’une fonction numérique, C. R. Acad. Sci. Paris, 222, 847
Cited by
Sorted by citations
Items 1,401–1,500 of 2,331
Wang, R., Chen, J., Gao, K., & Wei, G. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.08023 · Cited by 3
“Persistent homology, a branch of algebraic topology, is a powerful method for simplifying the structural complexity of macromolecules [1, 4, 41].”
BackgroundMethodOutlier-robust subsampling techniques for persistent homologyPreprint
Stolz, B. J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.14743 · Cited by 5
“Our method relies on computing the Vietoris–Rips filtration [4, 12] locally, in a small neighbourhood around each data point, where the afore mentioned computational problems of PH vanish.”
MethodStable Volumes for Persistent HomologyPreprint
Obayashi, I. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.11711 · Cited by 3
“Topological data analysis (TDA)[13, 5] is a data analysis method utilizing the mathematical concept of topology.”
MethodDynamical flexible inference of nonlinear latent structures in neural population activityPreprint
Abbaspourazad, H., Erturk, E., Pesaran, B., & Shanechi, M. M. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.03.13.532479 · Cited by 3
Bhaskar, D., Zhang, Y., Moore, J. L., Gao, F., Rieck, B., Wolf, G., Khasawneh, F. A., Munch, E., Noah, J. A., Pushkarskaya, H., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.03.22.533807 · Cited by 3
Application of Persistent Homology in Forecasting Realized VolatilityPreprint
Souto, H. G. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4484282 · Cited by 3
Sparse Portfolio Selection via Topological Data Analysis-Based ClusteringPreprint
Goel, A., Filipović, D., & Pasricha, P. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4711887 · Cited by 3
Topological data analysis of Chinese stocks’ dynamic correlations under major public events
Guo, H., Ming, Z., & Xing, B. (2023) · Frontiers in Physics · doi:10.3389/fphy.2023.1253953 · Cited by 2
Persistence Symmetric Kernels for Classification: A Comparative Study
Bandiziol, C., & Marchı, S. D. (2024) · Symmetry · doi:10.3390/sym16091236 · Cited by 2
Art’s hidden topology: A window into human perception
Dmitruk, E., Bajno, B., Kot, L., Dreszer, J., Bałaj, B., Ratajczak, E. K., Hajnowski, M., Janik, R. A., Kuś, M., Kadir, S., et al. (2026) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1014156 · Cited by 2
“Our exploration of topologically derived properties is grounded in their inherent independence from arbitrarily defined coordinates and metric attributes of perceived objects [44].”
Quantum community detection via deterministic elimination
Umeano, C., Scali, S., & Kyriienko, O. (2025) · Physical Review A · doi:10.1103/s7cm-c9qy · Cited by 3
Effective data reduction algorithm for topological data analysis
Choi, S., Oh, J., Park, J. R., Yang, S. Y., & Yun, H. (2025) · Applied Mathematics and Computation · doi:10.1016/j.amc.2025.129302 · Cited by 5
G-Mapper: Learning a Cover in the Mapper Construction
Alvarado, E., Belton, R., Fischer, E., et al. (2025) · SIAM Journal on Mathematics of Data Science · doi:10.1137/24m1641312 · Cited by 2
Bui, Q., Tien, T. N., Nguyen, T.-B., Nguyen, S. P., & Vo, B. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-92545-0_8 · Cited by 3
Hasan, M. M., Hossain, M. J., Waliullah, M., et al. (2025) · IEEE Access · doi:10.1109/access.2025.3583581 · Cited by 2
Alexander, A. W., & Wang, H. (2025) · PLOS Digital Health · doi:10.1371/journal.pdig.0000935 · Cited by 6
“Finally, to gain a deeper insight on hate speech and misinformation in the general context of mental health, we carry out a topological data analysis (TDA) on these embeddings to obtain an overarching topological mapping of the dataset [20–22].”
Jie, O. C., Zulkepli, N. F. S., Gobithaasan, R. U., Kasihmuddin, M. S. M., Naeeim, N. S. A., Noorani, M. S. M., & Musa, K. I. (2025) · Acta Tropica · doi:10.1016/j.actatropica.2025.107769 · Cited by 2
Dey, A. K., & Kundu, S. (2025) · Statistics in Medicine · doi:10.1002/sim.70109 · Cited by 2
Type-II neural symmetry detection with Lie theory
Gabel, A., Quax, R., & Gavves, E. (2025) · Scientific Reports · doi:10.1038/s41598-025-17098-8 · Cited by 2
“One can be interested in learning the fundamental geometrical properties of the distribution of data for various applications, ranging from data exploration and topological data analysis 13,14 to model selection and structural prior learning 15,16 .”
Change Point Detection in Financial Market Using Topological Data Analysis
Yao, J. X., Li, J., Wu, J., Yang, M., & Wang, X. (2025) · Systems · doi:10.3390/systems13100875 · Cited by 2
Exploring radiomic and topological features to evaluate treatment response in rectal cancer
Zhang, H., Liu, J., Rajamohan, N., Sanford, N. N., Khatri, G., Rodriguez, A. P., Rajaram, S., Jiang, S. B., Timmerman, R. D., Aguilera, T. A., et al. (2025) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ae117f · Cited by 2
Sato, G., Nakao, H., & Muolo, R. (2026) · The European Physical Journal Special Topics · doi:10.1140/epjs/s11734-026-02132-1 · Cited by 2
“One of the most promising applications of higher-order networks is in Topological Data Analysis (TDA) [33–36], which sets a rigorous mathematical framework to extract the shape of data.”
Herrero‐Perez, D., & Príncipe, J. (2025) · Computers & Structures · doi:10.1016/j.compstruc.2025.108041 · Cited by 2
Robust and Efficient Human Mobility Data Processing through the Lens of Topological Persistence
Lin, L., Ge, H., Michikata, T., et al. (2025) · ACM International Conference on Advances in Geographic Information Systems (SIGSPATIAL) · doi:10.1145/3748636.3764599 · Cited by 2
Persistent Homology Classifies Parameter Dependence of Patterns in Turing Systems
Spector, R., Harrington, H. A., & Gaffney, E. A. (2025) · Bulletin of Mathematical Biology · doi:10.1007/s11538-025-01552-9 · Cited by 4
“Thereare manyintroductionstotopologicaldataanalysis(EdelsbrunnerandHarer2010;Ghrist 2014;Carlsson2009),withfoundationsofalgebraictopology(Hatcher2002,Chapter 2),computationalalgebraictopologylecturenotes(Nanda2024)aswellasmaterial assumingonlylinearalgebra(Otteretal.2017;Schenck2022).”
Guo, M., Jia, P., Jin, Q., et al. (2025) · Engineering Computations · doi:10.1108/ec-08-2024-0781 · Cited by 2
A topological data analysis method for revealing dynamic changes in psychotherapy microprocesses
Luo, X., & Zhang, M. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2025.1711782 · Cited by 2
“Temporal Mapper is a tool based on Topological Data Analysis (TDA), a growing field in applied mathematics aiming to quantify the “shape” of high-dimensional objects (see Ghrist, 2007; Carlsson, 2009, and Munch, 2017 for an introduction).”
Sun, X., Zhuang, L., Gao, L., He, Q., Marinoni, A., & Zhang, B. (2026) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2026.3656937 · Cited by 3
“This focus grants it enhanced applicability in practical problem-solving scenarios [58].”
Zheng, S., Wang, B., Zhang, M., Tan, J., Lv, H.-B., Liu, R., Shi, Z.-Z., Li, X., Wang, R., Zhang, S. Q., et al. (2026) · Chemical Reviews · doi:10.1021/acs.chemrev.5c01098 · Cited by 4
Adaptive and distributed homology computation with application to large scale sensor networks
Zhang, M., Goupil, A., Colas, M., & Gellé, G. (2014) · International Conference on Wireless Communications and Signal Processing (WCSP) · doi:10.1109/wcsp.2014.6992039 · Cited by 2
“During the last decade, because of the emergence of the computational topology [1], [2], the mathematical theory of algebraic topology [3] becomes applicable to real situations for example in data analysis [4], [5], robotics [6], [7], networking [8], [9], [10] or sensor networks [11], [12].”
BackgroundPersistent Homology for Analyzing Environmental Lake Monitoring Data
Fraser, B. A., Wachowiak, M. P., & Wachowiak-Smolíková, R. (2016) · Springer eBooks · doi:10.1007/978-3-319-30379-6_22 · Cited by 2
Persistent homology of graph-like digital images
Ege, Ö., & Karaca, İ. (2020) · Annali di Matematica Pura ed Applicata (1923 -) · doi:10.1007/s10231-020-00962-x · Cited by 2
“In [12], a number of methods for thinking about data are given by using topological methods.”
MethodNotes on the values of volume entropy of graphs
Kim, W.-Y., & Lim, S. (2020) · Discrete and Continuous Dynamical Systems · doi:10.3934/dcds.2020221 · Cited by 2
Endo, Y., Hoshino, K., & Hamasuna, Y. (2020) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-020-02160-2 · Cited by 2
“In recent years, topological data analysis (TDA) (Edels-brunner and Harer 2008; Carlsson 2009; Weinberger 2011) has attracted attention as a method to extract the topological structure of data.”
A directed persistent homology theory for dissimilarity functions
Méndez, D., & Sánchez-García, R. J. (2023) · Journal of Applied and Computational Topology · doi:10.1007/s41468-023-00124-x · Cited by 2
“ent homology, prove some stability results, and discuss the computational aspects of our approach. 1. Introduction Persistent homology is one of the most successful tools in Topological Data Analysis [2], with recent applications in numerous scientific domains such as biology, medicine, neuroscience, robotics, and many…”
On the local behavior of spaces of range image patches
Li, J., & Xia, S. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09913-3 · Cited by 2
“For additional details, please refer to [5].”
Bose, E. L., Wood, L. J., & Wang, Q. M. (2020) · Topics in Stroke Rehabilitation · doi:10.1080/10749357.2020.1841439 · Cited by 2
“Instead of using individual parameters analyzed in isolation, TDA provides a systems-level analysis that considers the “shape”.(10,11) TDA specializes in understanding global features of high dimensional data that are not readily accessible using other techniques.”
BackgroundA density-based approach to feature detection in persistence diagrams for firn data
Lawson, A., Hoffman, T., Chung, Y.-M., et al. (2021) · Foundations of Data Science · doi:10.3934/fods.2021012 · Cited by 2
“Topological data analysis (TDA) is a rising field at the intersection of Mathematics, Statistics, and Machine Learning [14, 4, 25, 6].”
BackgroundMultiparameter Persistent Homology via Generalized Morse Theory
Bubenik, P., & Catanzaro, M. J. (2024) · Fields Institute communications · doi:10.1007/978-3-031-57204-3_4 · Cited by 3
Distributed Computation of Persistent Homology from Partitioned Big Data
Malott, N. O., Verma, R. R., Singh, R. P., & Wilsey, P. A. (2021) · IEEE International Conference on Cluster Computing (CLUSTER) · doi:10.1109/cluster48925.2021.00050 · Cited by 2
Topological data analysis for the energy and stability of endohedral metallofullerenes
Zhao, Y., Wang, Y., Ding, Y., & Han, H. (2021) · Journal of Mathematical Chemistry · doi:10.1007/s10910-021-01309-4 · Cited by 2
“[8, 9] and popularized by Carlsson’s paper [10] that later turned TDA into a hot field.”
BackgroundAlgar, S. D., Corrêa, D., & Walker, D. M. (2021) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0073247 · Cited by 2
Identifiers for structural warnings of malfunction in power grid networks
Andjelković, M., Maletić, S., & Tomanović, I. (2022) · Thermal Science · doi:10.2298/tsci220616115a · Cited by 2
Kozlov, D. N. (2022) · Journal of Applied and Computational Topology · doi:10.1007/s41468-022-00096-4 · Cited by 2
“counting the number of ways n objects can be partitioned into m non-empty groups, see Graham et al. (1994); Knuth (1997); Stirling (1749).”
BackgroundMethodInfluentialHomology groups of embedded fractional Brownian motion
Masoomy, H., Tajik, S., & Movahed, M. S. (2022) · Physical review. E · doi:10.1103/physreve.106.064115 · Cited by 2
Homology reveals significant anisotropy in the cosmic microwave background
Pranav, P., & Buchert, T. (2025) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202347761 · Cited by 2
Geometric and topological characterization of the cytoarchitecture of islets of Langerhans
Aggarwal, M., Striegel, D. A., Hara, M., & Periwal, V. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011617 · Cited by 3
“However, representative boundaries around these features are not unique by definition [50] so the computed boundary may be geometrically imprecise.”
BackgroundCentral limit theorem for euclidean minimal spanning acycles
Škraba, P., & Yogeshwaran, D. (2023) · Journal of Topology and Analysis · doi:10.1142/s1793525323500590 · Cited by 2
Cluster Persistence for Weighted Graphs
Bobrowski, O., & Škraba, P. (2023) · Entropy · doi:10.3390/e25121587 · Cited by 4
Spatiotemporal analysis of global grain trade multilayer networks considering topological clustering
Tu, Y., Shu, Z., Wu, W., et al. (2024) · Transactions in GIS · doi:10.1111/tgis.13149 · Cited by 2
“TDA is an emerging methodology that combines algebraic topology and data science (Chazal & Michel, 2021; Ghrist, 2008) and was first proposed by American scientist Gunnar Carlsson in 2009 (Carlsson, 2009).”
MethodAccelerating iterated persistent homology computations with warm starts
Luo, Y. X., & Nelson, B. J. (2024) · Computational Geometry · doi:10.1016/j.comgeo.2024.102089 · Cited by 6
Wasserstein distance loss function for financial time series deep learning
Souto, H. G., & Moradi, A. (2024) · Software Impacts · doi:10.1016/j.simpa.2024.100639 · Cited by 2
Towards building a trustworthy pipeline integrating Neuroscience Gateway and Open Science Chain
Sivagnanam, S., Yeu, S., Lin, K., et al. (2024) · Database · doi:10.1093/database/baae023 · Cited by 2
“…coupling measure values to generate algebraic topology structures such as simplicial complexes representing high-dimensional interaction patterns (28) using persistent homology methods that have been implemented in open-source libraries such as the GUDHI library (29) to generate these algebraic…”
MethodPlasmoData.jl — A Julia framework for modeling and analyzing complex data as graphs
Cole, D. L., & Zavala, V. M. (2024) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2024.108679 · Cited by 3
3D shape descriptor design based on HKS and persistent homology with stability analysis
He, Z., Zhuo, P., Lin, H., et al. (2024) · Computer Aided Geometric Design · doi:10.1016/j.cagd.2024.102326 · Cited by 2
Homology and Euler characteristic of generalized anchored configuration spaces of graphs
Kozlov, D. N. (2024) · Journal of Applied and Computational Topology · doi:10.1007/s41468-024-00167-8 · Cited by 2
“Our study lies within the field of Applied Topology, see (Carlsson 2009; Edelsbrunner and Harer 2010; Kozlov 2008, 2020) for more information.”
Combining Topological Signature with Text Embeddings: Multi-Modal Approach to Fake News Detection
Lavery, R., Jurek, A., & Bai, L. (2024) · Irish Signals and Systems Conference · doi:10.1109/issc61953.2024.10603336 · Cited by 2
“Short-lived features are generally considered noise, while long-lived features are considered robust features of the dataset [8].”
BackgroundMethodA distributed approach for persistent homology computation on a large scale
Ceccaroni, R., Rocco, L. D., Petrillo, U. F., et al. (2024) · The Journal of Supercomputing · doi:10.1007/s11227-024-06374-5 · Cited by 2
“This is especially true for Persistent Homology (PH) [17], a fundamental component of TDA, capable of constructing multi-resolution, noise-resilient topological features from a variety of different data clouds [8].”
Balaha, H. M., & Hassan, A. E.-S. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95439-6.00005-3 · Cited by 3
Model Poisoning Detection via Forensic Analysis
Monkam, G. F., & Bastian, N. D. (2024) · IEEE Military Communications Conference (MILCOM) · doi:10.1109/milcom61039.2024.10774017 · Cited by 2
“Persistent homology quantifies topological characteristics across multiple scales by computing the dataset’s homology groups [2].”
A computational framework for weighted simplicial homology
Bura, A. C., Dutta, N., Li, T. J. X., et al. (2024) · Topology and its Applications · doi:10.1016/j.topol.2024.109177 · Cited by 2
On the evolution of Betti curves in the cosmic web
Tymchyshyn, V., Tsizh, M., Vazza, F., et al. (2024) · Journal of Physical Studies · doi:10.30970/jps.28.4903 · Cited by 2
Homotopy types of Vietoris–Rips complexes of hypercube graphs
Feng, Z. (2025) · Journal of Topology and Analysis · doi:10.1142/s1793525325500062 · Cited by 2
Persistent Sheaf Laplacian Analysis of Protein Flexibility
Hayes, N., Wei, X., Feng, H., et al. (2025) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.5c01287 · Cited by 2
“39,40 However, persistent homology has several drawbacks, including its insensitivity to homotopic shape evolution.”
Park, S. A., Gurnari, D., Dłotko, P., Kim, Y., & Hahn, J. (2026) · npj Systems Biology and Applications · doi:10.1038/s41540-026-00763-7 · Cited by 2
“Additionally, Topological Data Analysis (TDA) 15 – 17 has emerged as a distinct mathematical framework that characterizes spatial organization through topologicalfeaturesratherthanconventionalstatistics.”
Sykes, J., Nicuşan, A. L., Werner, D., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-97175-0 · Cited by 2
“Specifically, TDA addresses how high-dimensional data structures can be represented in low-dimensions, and how discrete data points can be constructed into a global structure [18, 14].”
InfluentialTopological data analysis of the visibility graphs of the BTW avalanche-size sequences
Adami, V., Masoomy, H., & Najafi, M. N. (2025) · Journal of Physics A Mathematical and Theoretical · doi:10.1088/1751-8121/add4d4 · Cited by 3
“Features that persist longer are more likely to be real features of the space, not just caused by how we sample, measure, or choose parameters [48].”
Topological signal processing on quantum computers for higher-order network analysis
Leditto, C. M. G., Southwell, A., Tonekaboni, B., White, G. A. L., Usman, M., & Modi, K. K. (2025) · Physical Review Applied · doi:10.1103/physrevapplied.23.054054 · Cited by 2
“Indeed, topological data analysis (TDA) [21–24] is an umbrella term for a wide range of statistical methods that employ techniques from algebraic topology to study real-world data.”
BackgroundPham, T., Kouřimská, H. D. P., & Wagner, H. (2025) · Machine Learning and Knowledge Extraction · doi:10.3390/make7020048 · Cited by 2
The connectivity of Vietoris–Rips complexes of spheres
Adams, H., Bush, J., & Virk, Ž. (2025) · Journal of Applied and Computational Topology · doi:10.1007/s41468-025-00214-y · Cited by 2
“A popular way for data scientists to approximate the shape of a dataset is by thickening it, and then computing peristent homology [49, 50, 94].”
Modeling by topological data analysis and game theory for analyzing data poisoning phenomena
Феррара, М. (2025) · AIMS Mathematics · doi:10.3934/math.2025693 · Cited by 2
Chen, Y., & Lin, H. (2025) · Computer-Aided Design · doi:10.1016/j.cad.2025.103934 · Cited by 2
Fuzzy Simplicial Sets and Their Application to Geometric Data Analysis
Barth, L. S., Fahimi, H., Joharinad, P., et al. (2025) · Applied Categorical Structures · doi:10.1007/s10485-025-09827-x · Cited by 2
“One important example of such higher relationships are those that arise in the area of persistent homology (see, for example [Carlsson, 2009]), where a simplicial complex or simplicial set can encode the relationships that arise due to a filtration.”
Bhattacharya, D., Sinha, N., Yashwanth, R., & Chattopadhyay, A. (2025) · Scientific Reports · doi:10.1038/s41598-025-20635-0 · Cited by 2
“The present study utilizes persistence diagram to derive relevant descriptors for quantifying the dissimilarities in the topological properties of visual networks, representing COCO, ImageNet, and SUN, as obtained from each fMRI session.”
Node persistence from topological data analysis reveals changes in brain functional connectivity
Mondal, M., Yadav, Y., Jost, J., & Samal, A. (2025) · Patterns · doi:10.1016/j.patter.2025.101427 · Cited by 2
Asadollahi, J., Sadeghi, S., & Treffinger, H. (2024) · Canadian Journal of Mathematics · doi:10.4153/s0008414x24000221 · Cited by 2
“We refer the reader to the survey [17] by Carlsson, or the book [29] by Oudot, for an introduction to TDA and connections to quiver representations.”
On Computability and Triviality of Well Groups
Franek, P., & Krčál, M. (2016) · Discrete & Computational Geometry · doi:10.1007/s00454-016-9794-2 · Cited by 2
“We can only name a fraction of the instances: stability in data analysis [7], robust optimization [3], image processing [17], or stability of numerical methods [19].”
BackgroundAn Analysis of Spaces of Range Image Small Patches
Yin, Q., & Wang, W. (2015) · The Open Cybernetics & Systemics Journal · doi:10.2174/1874110x01509010275 · Cited by 2
“We will regard the m m blocks as got though sampling a real valued smoothing function on the xy-plane at m m mesh points.”
MethodPTS: Projected Topological Stream clustering algorithm
Pereira, C. M. M., & de Mello, R. F. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.08.094 · Cited by 2
A topological approach to spectral clustering
Rieser, A. (2021) · Foundations of Data Science · doi:10.3934/fods.2021005 · Cited by 2
“We encounter a more topologically-oriented approach in [3], in which persistent homology [14, 7, 2] is used to help a statistician identify high-density regions of a distribution function.”
MethodBPS Spectra, Barcodes and Walls
Universit&, Cirafici, M., #224, & di Trieste, T. (2019) · Symmetry Integrability and Geometry Methods and Applications · doi:10.3842/sigma.2019.052 · Cited by 2
“The main idea is to use certain criteria to select only a certain subset L of the point cloud X, which is then used to form a simplicial complex which approximates the Vietoris–Rips complex [4].”
BackgroundMethodInfluentialGinzburg, V. L., & Ṗasechnik, D. V. (2017) · Arnold Mathematical Journal · doi:10.1007/s40598-016-0062-6 · Cited by 2
“This filtration is extremely important and, in particular, it allows one to treat Floer theory in the context of persistent homology and topological data analysis; see Carlsson (2009); Ghrist (2008).”
BackgroundSample Complexity in Manifold Learning
Narayanan, H. (2011) · CRC Press eBooks · doi:10.1201/b11431-5 · Cited by 2
Progress in Persistence for Shape Analysis (Extended Abstract)
Ferri, M. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-39441-1_1 · Cited by 2
The Vietoris-Rips Complexes of Finite Subsets of an Ellipse of Small Eccentricity
Reddy, S. (2017) · DukeSpace (Duke University) · Cited by 2
Spatial objects classification algorithm on the basis of topological features of a form
Eremeev, S. V., & Kuptsov, K. V. (2017) · Russian-Pacific Conference on Computer Technology and Applications (RPC) · doi:10.1109/rpc.2017.8168065 · Cited by 2
Bhattacharya, R. N., & Lin, L. (2019) · Springer proceedings in mathematics & statistics · doi:10.1007/978-981-15-0298-9_1 · Cited by 3
“Dey and Li (2009)whouse thefirst homologygrouptoidentifyhandlesandholesina closedbounded domain in 3D. For a more elaborate technique using algebraic topology, known as persistent homology,we refer to Carlsson (2009). 9. EXAMPLES AND APPLICATIONS In this section we apply the theory to a numberof data sets available in…”
MethodInfluentialThe Cylinder of a Relation and Generalized Versions of the Nerve Theorem
Fernández, X., & Minian, E. G. (2018) · Discrete & Computational Geometry · doi:10.1007/s00454-018-0028-7 · Cited by 2
Exploring User Capability Data with Topological Data Analysis
Persad, U., Goodman-Deane, J., Langdon, P., et al. (2018) · Springer eBooks · doi:10.1007/978-3-319-75028-6_4 · Cited by 2
“This method combines the mathematical field of Topology with Machine Learning and Visualisation resulting in TDA (Carlsson 2009; Lum et al. 2013).”
MethodActive Sampling for Subjective Video Quality Assessment
Jiang, Y., Xu, Q., Zhang, W., et al. (2018) · IEEE International Conference on Multimedia Big Data · doi:10.1109/bigmm.2018.8499064 · Cited by 2
“However, for a given graph instance, how to ensure that this graph could satisfy these two conditions (connected & loopfree)? Fortunately, persistent homology [34], [35] provides us”
BackgroundOn the Logistic Behaviour of the Topological Ultrametricity of Data
Bradley, P. E. (2018) · Journal of Classification · doi:10.1007/s00357-018-9281-y · Cited by 2
On the Nonlinear Statistics of Optical Flow
Adams, H., Bush, J., Carr, B., Kassab, L., & Mirth, J. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-10828-1_12 · Cited by 2
Topological Data Analysis for the String Landscape
Cole, A., & Shiu, G. (2018) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.48550/arxiv.1812.06960 · Cited by 2
Computational topology: Isotopic convergence to a stick knot
Li, J., Peters, T., Jordan, K. E., et al. (2016) · Topology and its Applications · doi:10.1016/j.topol.2016.03.032 · Cited by 2
“ere is an example of a PL structure that becomes self-intersecting while the associated Bezier curve remains simple Cassidy et al. (2014). Recent perspectives on computational topology have appeared Carlsson (2009); Edelsbrunner and Harer (2010); Zomorodian (2005). 2. Inserting Midpoints as Control Points The fundamen…”
A Topological Data Analysis Approach to Video Summarization
Hu, C.-S., & Yeh, M.-C. (2019) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2019.8803142 · Cited by 2
“Homologies and Betti numbers of a simplicial complex can be computed via persistent homology [10, 12].”
BackgroundOn the law of the iterated logarithm and strong invariance principles in stochastic geometry
Krebs, J. (2021) · Bernoulli · doi:10.3150/20-bej1288 · Cited by 2
“, connected components, see Chatterjee and Sen (2017), who quantify the stabilization for the minimal spanning tree. The methods of TDA have been successfully applied in the past to various fields such as finance (Gidea and Katz (2018)), material science (Lee et al. (2017)) or biology (Yao et al.”
BackgroundMethodInfluentialSynthesis of Energy-Bounded Planar Caging Grasps using Persistent Homology
Mahler, J., Pokorny, F. T., Niyaz, S., et al. (2020) · Springer proceedings in advanced robotics · doi:10.1007/978-3-030-43089-4_27 · Cited by 2
Multiview topological data analysis for crowdsourced service supply-demand gap prediction
Said, A. B., & Erradi, A. (2020) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc48107.2020.9148097 · Cited by 2
“Recently, Topological Data Analysis (TDA) [12] has emerged in machine learning.”
BackgroundAdvances in Geometrical Analysis of Topologically-Varying Shapes
Srivastava, A., Guo, X., & Laga, H. (2020) · doi:10.1109/isbiworkshops50223.2020.9153426 · Cited by 2
“Shapes of planar, parameterized curves β : [0, 1] → R are analyzed using their square-root velocity functions (SRVFs) q : [0, 1] → R (see [2] for definition of SRVFs).”
BackgroundMethodKarimova, L., Terekhov, A., Makarenko, N., et al. (2020) · Cogent Engineering · doi:10.1080/23311916.2020.1808340 · Cited by 2
“At the same time, the lifetimes of each point prior to its inclusion in the edge and of each hole until its disappearance (painting) are different (Carlsson, 2009).”
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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