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.”
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Cited by
Sorted by citations
Items 2,001–2,100 of 2,331
Applications of the Topological Data Analysis in Real Life
Rida, S. Z., Noreldeen, A. H., & Karar, F. R. (2022) · WSEAS TRANSACTIONS ON MATHEMATICS · doi:10.37394/23206.2023.22.3
Cancer Fingerprints by Topological Data Analysis
Carpio, A. (2022) · Mathematics in industry · doi:10.1007/978-3-031-11818-0_4
Topology recapitulates ontogeny of dendritic arborsPreprint
Liao, M., Bird, A. D., Cuntz, H., & Howard, J. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.02.27.530331
“…which is an approach to the analysis of data using techniques from topology to obtain information that is independent of the particular metrics (Carlsson, 2009); this approach can reveal a system’s intrinsic structure and distinguish that structure from other structures and noise (Horak et…”
MethodTopological Inference on Electroencephalography
Wang, Y. (2023) · Springer eBooks · doi:10.1007/978-981-99-0803-5_24
Comparison of complex dynamical systems based on topological data analysis
Chukanov, S. N. (2023) · Computer Research and Modeling · doi:10.20537/2076-7633-2023-15-3-513-525
Joharinad, P., & Jost, J. (2023) · Mathematics of data · doi:10.1007/978-3-031-33440-5_9
Topological Foundations, Hypercomplexes and Homology
Joharinad, P., & Jost, J. (2023) · Mathematics of data · doi:10.1007/978-3-031-33440-5_2
Topological variability in financial markets
Valdivia, A. D. (2023) · Quantitative Finance and Economics · doi:10.3934/qfe.2023019
“While the topology of a point cloud is trivial, topological data analysis, developed in Carlsson (2009), allows us to view point clouds as simplicial manifolds.”
MethodInfluentialFederated Topology Preserving Domain Adaptation for Human Activity Recognition
Bukit, T. A., Pillado, E. P. B., Lee, S., & Yahya, B. N. (2023) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu59756.2023.10223939
“This branch of data science defines structure and shape within datasets by profiling the data as point clouds with a notion of distance or similarity in a coordinate free approach [10].”
BackgroundTopological Characteristics of 5d Spatially Dynamic Brain Networks in Schizophrenia
Salman, M. S., Iraji, A., Lewis, N. L., et al. (2023) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi53787.2023.10230513
“Topological data analysis (TDA) is an emerging field motivated by the application of algebraic topology and computational geometry to complex data analysis [6].”
BackgroundFinding Antibodies in Cryo-EM densities with CrAIPreprint
Mallet, V., Rapisarda, C., Minoux, H., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.27.559736
“Once the model makes a prediction, persistence diagrams [6, 7] are used to select the relevant results which are transformed into a PDB file.”
MethodComment on egusphere-2023-1855
Fuchs, M., Suzuki, A., Hasumi, T., et al. (2023) · doi:10.5194/egusphere-2023-1855-rc2
Wasserstein Distance and Realized VolatilityPreprint
Souto, H. G., & Moradi, A. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4595110
Topological data analysis of human vowels: Persistent homologies across representation spaces
Bonafos, G., Pudlo, P., Freyermuth, J., Tronçon, S., & Rey, A. (2026) · Speech Communication · doi:10.1016/j.specom.2026.103363 · Cited by 1
Determinantal Varieties From Point Configurations on Hypersurfaces
Caminata, A., Moon, H., & Schaffler, L. (2023) · International Mathematics Research Notices · doi:10.1093/imrn/rnad244 · Cited by 1
Topological Estimation of Number of Sources in Linear Monocomponent Mixtures
Kennedy, S., Tummala, M., & McEachen, J. (2023) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco58844.2023.10289812
“Homology (PH) [16], which has been used to study the topology of datasets with many real-world applications [17]– [20].”
Typed topology and its application to data sets
Hu, W. (2023) · Topology and its Applications · doi:10.1016/j.topol.2023.108760
“For instance, how to describe connectedness and closure in a finite data set? In 2009, Carlson published the article ”Topology and data” [1], which provided a survey of using algebraic topological tools on data sets.”
BackgroundInfluentialDiscovering Secondary Protein Structures via Local Euler CurvaturePreprint
Moreira, R. A., Braddell, R., Santos, F. A. N., Fülöp, T., Desroches, M., Ubarretxena‐Belandia, I., & Rodrigues, S. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.11.27.568841
“…that provide efficient algorithms to syn-48 thesise highly complex nonlinear high-dimensional data as geometrical objects (‘shapes’), which satisfy proper-50 ties (called Topological invariants ) remaining unchanged under continuous deformations (e.g. stretching, twisting, or bending) 19–24 .”
BackgroundA Topological Approach to Credit ScoringPreprint
Charmpi, M., Giannouli, P., & Xanthopoulos, S. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4629581
A Topological Data Analysis of the Protein Structure
Lamine, Z., Mamouni, M. I., & Mansouri, M. W. (2023) · International Journal of Analysis and Applications · doi:10.28924/2291-8639-21-2023-136
“…of persistent homology comes from its capacity of neglecting metric details and capturing void, cavities and holes at different scales by using a filtration parameter [8] [9] which is the truly demanded function from the mathematical tools used in the analysis of protein structure and binding sites.”
MethodBlockchain Energy Trade System
Ikeda, Y. (2023) · Evolutionary economics and social complexity science · doi:10.1007/978-981-99-8779-5_10
Algebraic machine learning with an application to chemistry
Sai, E. E., Gara, P., & Pflaum, M. J. (2024) · Foundations of Data Science · doi:10.3934/fods.2024004
“Hence dim( R alg ( Z alg ( S ))) = dim( R ( Z ( S ))) , which coincides with dim( Z ( S )) .”
BackgroundTopology-enhanced machine learning for speech signal processing
Feng, P., Qu, Q., Zhang, H., Yi, S., Yu, Z., Ding, Z., & Zhu, Y. (2026) · Nature Communications · doi:10.21203/rs.3.rs-3978261/v1
Micro-nodule analysis by severity of pneumoconiosis using 3D CT images
Nii, R., Kawata, Y., Ohtsuka, Y., et al. (2024) · SPIE Medical Imaging: Imaging Informatics for Healthcare, Research, and Applications · doi:10.1117/12.3009095
“Topological data analysis (TDA) is a promising approach employed to measure high-dimensional architectures that extract the shape of a dataset based on spatial connectivity 15–17 .”
BackgroundThe Internal Topology of Rocks
Korvin, G. (2024) · Earth and environmental sciences library · doi:10.1007/978-3-031-46700-4_3
Forecasting Financial Crisis using Topological Data Analysis Approach
Oseko, N. N., Omondi, A. G., Onyango, H. D., et al. (2024) · African scientific annual review. · doi:10.51867/asarev.maths.1.1.1
“TDA is a recent field emerging from various in applied (algebraic) topology and computational geometry during the first decade of the century [5].”
BackgroundInfluentialDo We Need More Metrics for Description of Atomic Force Microscopy Data for Nanostructured Surfaces?Preprint
Aglikov, A. S., Жуков, М. В., Aliev, T. A., Kozodaev, D. A., Nosonovsky, M., & Скорб, Е. В. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4820458
Sparse Higher Order Čech Filtrations
Buchet, M., DORNELAS, B. B., & Kerber, M. (2024) · Journal of the ACM · doi:10.1145/3666085
Spectral decomposition unlocks ascidian morphogenesisPreprint
Dokmegang, J., Faure, E., Lemaire, P., et al. (2024) · eLife · doi:10.7554/elife.94391
Bigraded Betti numbers and generalized persistence diagrams
Kim, W., & Moore, S. (2024) · Journal of Applied and Computational Topology · doi:10.1007/s41468-024-00180-x · Cited by 7
“…2016; Lesnick and Wright 2015) in part because 2-parameter filtrations arise in the study of interlevel set persistence (Botnan and Lesnick 2018; Carlsson et al. 2009), in the study of point cloud data with non-uniform density (Bauer et al. 2020; Cai et al. 2021; Carlsson and Mémoli 2010;…”
Integrating Image Visibility Graph and Topological Data Analysis for Enhanced Texture Classification
Pal, R., Kumar, S., & Singh, M. K. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-3292-0_40
Schrader, P. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4885393
Topological Structures in the Space of Treatment-Naïve Patients with Chronic Lymphocytic Leukemia
McGee, R. L., Reed, J., Coombes, C. E., Herling, C., Keating, M. J., Abruzzo, L. V., & Coombes, K. R. (2024) · Cancers · doi:10.3390/cancers16152662
“In the early 2000s, mathematicians introduced an idea that is now called TDA [12–15], implemented using a notion called persistent homology [16,17].”
BackgroundResearch on Clothing Image Retrieval Combining Topology Features with Color Texture Features
Zhang, X., Sun, H., & Ma, J. (2024) · Mathematics · doi:10.3390/math12152363
Quantum HodgeRank: Topology-based rank aggregation on quantum computers
Leditto, C. M. G., Southwell, A., Tonekaboni, B., Usman, M., & Modi, K. K. (2025) · Physical Review Applied · doi:10.1103/m6nc-ypl7
Interpreting Deep Patient Stratification Models with Topological Data Analysis
Jurek, A., Gault, R., Ahmaderaghi, B., Fahim, M., & Bai, L. (2024) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-031-62502-2_65
High-Dimensional Bayesian Optimization via Random Projection of Manifold Subspaces
Nguyen, Q.-A. H., & Tran, T. H. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-70371-3_17
Use of Riemannian Distance Metric to Verify Topological Similarity of Acoustic and Text Domains
Yessenbayev, Z., & Kozhirbayev, Z. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72350-6_25
PHLOWER - Single cell trajectory analysis using Hodge DecompositionPreprint
Cheng, M., Jansen, J., Reimer, K. C., Grande, V., Nagai, J. S., Li, Z., Kießling, P., Graßhoff, M., Kuppe, C., Schaub, M. T., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.01.613179
“Remark: The filtering of edges by distance to obtain the triangulated graph is related to persistent homology analysis 62 .”
Exploring Topological Information Beyond Persistent Homology to Detect Geospatial Objects
Syzdykbayev, M., & Karimi, H. A. (2024) · Remote Sensing · doi:10.3390/rs16213989
Mitigating bias in radiology: The promise of topological data analysis and simplicial complexes
Singh, Y., Farrelly, C., Hathaway, Q. A., & Carlsson, G. (2024) · Oncotarget · doi:10.18632/oncotarget.28668
Self-citationPersistent homology diagram (PHD) based web service for cancer tagging of mammograms
Ghosh, A., Ranjan, P., Shrivastav, K. D., et al. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-15452-2.00021-2
Can We Determine Whether a Set of Ethereum Transaction Data Contains Fraudulent Transactions?
Nakatani, S., Kuzuno, H., Takita, M., Mohri, M., & Shiraishi, Y. (2024) · IEEE Conference on Dependable and Secure Computing (DSC) · doi:10.1109/dsc63325.2024.00023
“Topological data analysis is a method for characterizing data by analyzing the geometric structure based on the topological information of a set of points arranged in space [6].”
A Dynamical Systems Approach for Detecting Cyberattacks in Software-Defined Networks
Fouad, H. Y., Velazquez, A., & Moskowitz, I. S. (2024) · IEEE Military Communications Conference (MILCOM) · doi:10.1109/milcom61039.2024.10773894
Neural Topic Model with Distance Awareness
Chen, S., Zhao, H., Huynh, V. H., Phung, D., & Cai, J. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78189-6_22
A Vectorization Method Induced By Maximal Margin Classification For Persistent Diagrams
Wu, A., Pan, Y. R., Zhou, F., et al. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10821901
“By popularizing this idea, researchers have developed a major technique in TDA called persistent homology [4], [13].”
MethodTozzi, A. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.04.636494
Leveraging persistent homology for liver tumour classification
Ali, D., Peoples, J., Mojtahedi, R., et al. (2025) · SPIE Medical Imaging: Computer-Aided Diagnosis · doi:10.1117/12.3045640
The search for CT imaging subtypes of colorectal liver metastases and the impacts of slice thickness
Kobayashi, K. S., Ali, D. A., Mojtahedi, R., Peoples, J., Hamghalam, M., Gangai, N. C., Gönen, M., Gian, R. K., Chun, Y. S., Kang, H. C., et al. (2025) · SPIE Medical Imaging: Computer-Aided Diagnosis · doi:10.1117/12.3048815
Filipino sign language alphabet recognition using Persistent Homology Classification algorithm
Jetomo, C. B., & Lara, M. L. D. D. (2025) · PeerJ Computer Science · doi:10.7717/peerj-cs.2720
“It can be used for determining invariant features or topological properties of a space of points that persist across multiple resolutions ( Carlsson, 2009 ; Edelsbrunner & Harer, 2008 ).”
Unsupervised Assessment of Landscape Shifts Based on Persistent Entropy and Topological Preservation
Basterrech, S. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-82346-6_9 · Cited by 1
The Mathematics of Geometric and Topological Data AnalysisPreprint
Alonso, M. N. I. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5082309
Huang, C., Guo, F., Lin, S., Dai, Y., Chen, Q., Zhang, S., & Xu, X. (2025) · Medical Physics · doi:10.1002/mp.17707
“26 To provide a more specific example, in medical imaging, loop structures can often be observed The PD of certain image.”
The Topology of Evasion: Persistent Homology of Gene Repertoires of ImmunityPreprint
Hampton, M. E., & Mamudu, I. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.27.640626
“There is an excellent survey of topological data analysis focused on genomic applications by Rabad´an and Blumberg [38]; we also recommend the more general articles [19, 8, 34, 31] and the text [9].”
Ser, J. D., Martínez-Seras, A., Laña, I., Bilbao, M. N., Fafoutellis, P., & Vlahogianni, E. I. (2024) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc58415.2024.10920176
“Topological data analysis (TDA) aims at extracting meaningful insights from complex datasets by studying their underlying topological structure [28].”
Suraj, T. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5198824
Moon, T. J., Park, S. M., Yoon, H. G., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-95495-9
A Quick Trip Through Group-equivariant Non-expansive Operators
Ahmad, F., & Frosini, P. (2025) · Springer proceedings in mathematics & statistics · doi:10.1007/978-981-96-1505-6_30
Abstract simplicial complexes in Macaulay2
Grieve, N. (2025) · Journal of Software for Algebra and Geometry · doi:10.2140/jsag.2025.15.29
Classification of Some Cosmological Images Using Deep Learning and Persistent Homology
Sekuloski, P., & Ristovska, V. D. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-86162-8_11
Classification of epilepsy MEG data using topological data analysis and artificial neural network
Han, Y., Chung, C. K., & Kim, J. (2025) · Korean Journal of Applied Statistics · doi:10.5351/kjas.2025.38.2.177
Mestiri, M., Fergombé, A., & Huftier, A. (2025) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-031-87763-6_36
A Hamiltonian Higher-Order Elasticity Frame work for Dynamic Diagnostics(2HOED)Preprint
Tiwang, G. N. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5235252
Comparison Between Poisson and Telegraph Models
Majhi, A. (2025) · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · doi:10.55041/ijsrem48184
Topo Goes Political: TDA-Based Controversy Detection in Imbalanced Reddit Political Data
Arun, A., Chandra, K. K., Sinha, A., et al. (2025) · doi:10.1145/3701716.3717535
“To capture the evolving nature of discussions, we utilize techniques from Topological Data Analysis, particularly Persistent Homology [4] back-and-forth exchanges among a small group of users can form loops in the user-interaction graph.”
Campi, M., Peters, G. W., Morvan, P., et al. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5264112
Topological Data Analysis with Applications
Intermont, M. (2025) · The Mathematical Intelligencer · doi:10.1007/s00283-025-10419-9
“Clustering methods by definition cannot model continuous phenomena.”
Comparing persistent homology-based classifiers for Filipino sign language recognition
Jetomo, C. B., & Lara, M. L. D. D. (2025) · Mathematical Modeling and Computing · doi:10.23939/mmc2025.02.512
Homology localization through the looking-glass of parameterized complexity theory
Blaser, N., & Vågset, E. R. (2025) · Journal of Applied and Computational Topology · doi:10.1007/s41468-025-00212-0
“Topological features are often preferred over purely geometric features because they give qualitative information and reduce the reliance on poorly justified choices of coordinate systems or metrics, leading to more robust results [6].”
Turning Time Into Shapes: A Point‐Cloud Framework With Chaotic Signatures for Time Series
Singh, P., & Raman, B. (2025) · Journal of Forecasting · doi:10.1002/for.3287
Topological data analysis of coda waves to classify microstructural variability
Sharma, S., Khasawneh, F. A., Wall, J. J., Seuaciuc-Osório, T., & Chakrapani, S. K. (2025) · NDT & E International · doi:10.1016/j.ndteint.2025.103437
Curse of dimensionality on persistence diagrams
Hiraoka, Y., Imoto, Y., Kanazawa, S., et al. (2025) · Foundations of Data Science · doi:10.3934/fods.2025008
Netshifhire, R., & Visaya, M. V. (2024) · International Conference on Sustainable Technology and Engineering (i-COSTE) · doi:10.1109/i-coste63786.2024.11024568
Wenzel, A. R., Haughey, P. M., Nguyen, K., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.13.659586
“Specifically, the k -th Betti number is defined as the rank of the k -th homology group, which quantifies the number of k -dimensional holes enclosed by V εR ( X f ) [37].”
Spectral decomposition unlocks ascidian morphogenesis
Dokmegang, J., Faure, E., Lemaire, P., et al. (2025) · eLife · doi:10.7554/elife.94391.3
Esteve, M., Martinez-Gracia, A., Rodriguez-Sala, J. J., & Falcó, A. (2025) · SoftwareX · doi:10.1016/j.softx.2025.102222
Motor Fault Detection with a Hybrid Physics-based and Data-Driven Method
Ji, D.-Y., Wang, B., Inoué, H., et al. (2025) · doi:10.1109/iemdc60492.2025.11061000
“4) Persistent homology [12] is derived through a filtration process of the Rips complex.”
Topological Persistent Homology for AI-Powered Retinal Image Classification
Hefny, M. S., & Shalan, A. M. (2025) · International Conference on Artificial Intelligence, Robotics and Control (AIRC) · doi:10.1109/airc64931.2025.11077499
“This paper proposes using Persistent Homology [8], a topological data analysis methodology, for analyzing retinal images.”
Chinesta, F., Cueto, E., Champaney, V., et al. (2025) · Studies in big data · doi:10.1007/978-3-031-87572-4_9
Topology-based analysis and optimization for agent fleet behavior
Perera-Lago, J., Toscano-Durán, V., Torras-Casas, Á., & González-Dı́az, R. (2025) · Open Research Europe · doi:10.12688/openreseurope.20760.1 · Cited by 1
Time-Resolved 4D STEM Reveals Facilitated Dynamics in a Multicomponent Metallic Glass Forming Liquid
Huang, S., Qin, S., Berthier, L., et al. (2025) · HAL (Le Centre pour la Communication Scientifique Directe) · doi:10.1093/mam/ozaf048.801
Big Data Searching Using Words
Acharjee, S., & Choudhury, R. (2025) · New Mathematics and Natural Computation · doi:10.1142/s179300572550053x
Zhang, Y., Zhang, C., Tian, J.-W., et al. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3595846
“2) Theoretical Foundation of Circular Structure: Circular structures [42], [43] have special properties in graph theory [44], [45] and topology.”
A Forensic Analysis Framework for Machine Learning Model Poisoning Detection
Monkam, G. F., Yan, J., & Bastian, N. D. (2025) · Security and Privacy · doi:10.1002/spy2.70079
Merging Fuzzy Simplicial Sets and Metric Spaces: A Category Theoretical Approach
Barth, L. S., Fahimi, H., Joharinad, P., et al. (2025) · Mathematics of data · doi:10.1007/978-3-031-97973-6_4
Barth, L. S., Fahimi, H., Joharinad, P., et al. (2025) · Mathematics of data · doi:10.1007/978-3-031-97973-6_1
Shami, A. A., Bachri, M., Young, C., & Grissom, D. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-99965-9_37
Optimization of Maggot mass rearing via topological statistics approach
de Melo, T., Rosa, R., Pereira, J. V., et al. (2025) · Boletim da Sociedade Paranaense de Matemática · doi:10.5269/bspm.76009
DeepAtlas: a tool for effective manifold learningPreprint
Hughes, S., Hamilton, T., Kolokotrones, T., & Deeds, E. J. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.26.672474
“Finally, future work will be needed to further explore DeepAtlas’ generative capabilities, and to investigate new applications for the output model, including leveraging the model for direct application of ideas from differential geometry and topological data analysis (51, 52).”
Vietoris–Rips Complexes of Torus Grids
Adams, H., Adetowubo, A. Y., Barriga-Acosta, H., et al. (2025) · Mediterranean Journal of Mathematics · doi:10.1007/s00009-025-02945-9
“More recently, Vietoris–Rips complexes have been used in computational algebraic topology and topological data analysis to process point cloud data [13, 21, 20, 19, 49], based on the existence of fast algorithms to compute persistent homology [6, 50, 51].”
Schonsheck, N., & Giusti, C. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.27.672728
“This framework dovetails with computational tools from topological data analysis [29, 30, 31] which provide techniques for detecting and quantifying circular topologies, including a newly developed technique called the method of analogous cycles (MAC) [32] for matching topological structure across…”
Parallel computation of interval bases for persistence module decomposition
Gregorio, A. D., Guerra, M., Scaramuccia, S., & Vaccarino, F. (2025) · Applicable Algebra in Engineering Communication and Computing · doi:10.1007/s00200-025-00699-1 · Cited by 4
“…Module is a modern name for finite-dimensional representations of an equioriented quiver of type A n that has become popular within the setting of Topological Data Analysis (TDA) and, more specifically, in connection to Persistent Homology , one of the most successful tools in TDA (see [10]).”
Geometric bounds for persistence
Balitskiy, A., Coşkunuzer, B., & Mémoli, F. (2025) · Transactions of the American Mathematical Society · doi:10.1090/tran/9514
Liu, C., Yang, G., & Liu, J. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5445925
Time Series Analysis of Spiking Neural Systems via Transfer Entropy and Directed Persistent HomologyPreprint
Peek, D., Pritam, S., Skerritt, M. P., & Chalup, S. K. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5459120
From Darwin to Teleology: A Categorical Final--Cause Calculus for EvolutionPreprint
Patrascu, A. T. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5448115
Topology-Aware Deep Models for Skin Lesion Classification
Chakraborty, S., Koung, P., & Coşkunuzer, B. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-05825-6_4
Exponential Dissimilarity-Dispersion Family for Domain-Specific Representation Learning
Togo, R., Nakagawa, N., Ogawa, T., & Haseyama, M. (2025) · IEEE Transactions on Image Processing · doi:10.1109/tip.2025.3608661 · Cited by 1
“Because the motivation of this assumption is the manifold hypothesis [15], [16], [45], it induces a formulation in which the data variable x is supported on a di ff erentiable data manifold X ⊆ R M with a local coordinate system referred to as a latent space Z ⊆ R L .”
A feature fusion method based on topological data analysis and convolutional neural network
Yang, H. E., Guangjun, Q. I. N., Liu, Z., Hu, Y., Guangnan, L. I. U., & Qinglong, D. A. I. (2025) · JOURNAL OF SHENZHEN UNIVERSITY SCIENCE AND ENGINEERING · doi:10.3724/sp.j.1249.2025.05624 · Cited by 2
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-11, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
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