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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Cited by
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Items 1,601–1,700 of 2,331
StripePy: fast and robust characterization of architectural stripes
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Topological Data Analysis and Graph-Based Learning for Multimodal Recommendation
Bachiri, K., Yahyaouy, A., Malek, M., & Rogovschi, N. (2025) · IEEE Access · doi:10.1109/access.2025.3582480 · Cited by 3
“TDA offers a mathematical framework for extracting robust structural representations from high-dimensional and multimodal data [16].”
InfluentialA new quantum computational set-up for algebraic topology via simplicial sets
Zucchini, R. (2025) · Journal of AppliedMath · doi:10.59400/jam3011 · Cited by 1
Topological and geometric characterization of synthetic aperture sonar collections
Robinson, M., Memon, Z., & Gualtieri, M. (2025) · The Journal of the Acoustical Society of America · doi:10.1121/10.0037085 · Cited by 2
Novel Statistical and Topological Data Analyses of 2D Electronic Images
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“This is the motivation for using algebraic topological methods to study complicated data sets ([5]).”
Strategic Interplay: Game-Theoretic Frameworks for Topological Robustness Against Data Poisoning
Феррара, М. (2025) · WSEAS Transactions on Information Science and Applications archive · doi:10.37394/23209.2025.22.44 · Cited by 1
Moreira, R. I. Y., & Morejón, M. F. O. (2025) · Revista de Investigación Desarrollo e Innovación · doi:10.19053/uptc.20278306.v15.n2.2025.19629 · Cited by 1
Euler Characteristic Surfaces: A Stable Multiscale Topological Summary of Time Series Data
Roy, A., Mitra, A., & Dutta, T. (2025) · La Matematica · doi:10.1007/s44007-025-00165-x · Cited by 2
“Having initially started with explorations of static data clouds [1–3], complex data from different kinds of dynamical systems are being studied with this approach, e.g. biological aggregation [4], social systems, gene networks [5, 6], economics/financial data [7, 8], power engineering [9] etc.”
Contextualized LSTM-DNN Model for County-Level Corn Yield Prediction from Weather and Soil Data
Basir, M. S., Li, Y. S., Kong, N. N., Castiblanco, F. A., Krogmeier, J. V., & Buckmaster, D. R. (2025) · IEEE Latin Conference on IoT (LCIoT) · doi:10.1109/lciot64881.2025.11118506 · Cited by 1
“Zigzag topology persistence (ZZ-Top persistence), an advanced method in topological data analysis (TDA), tracks the evolution of features in data over time intervals, even as data changes [12].”
Yu, X., & Huang, Z. (2025) · International Conference on Big Data and Privacy Computing (BDPC) · doi:10.1109/bdpc63545.2025.11135803 · Cited by 1
“• For a point cloud X , the Vietoris-Rips complex V R ( X, r ) at radius r contains a simplex for every set of points with pairwise distances ≤ r [4].”
InfluentialFactorization for collider dataspace correlators
Larkoski, A. J. (2025) · Physical review. D/Physical review. D. · doi:10.1103/6r43-xhtj · Cited by 1
ToBaFu: Topology-based fusion model for classification of two-dimensional cancer images
Xing, Y., Chen, H., & Zheng, Q. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.108117 · Cited by 1
Marchetti, G. (2025) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0293702 · Cited by 1
“The cosine distance reads [64]”
Islambekov, U., & Luchinsky, A. (2025) · The Journal of Open Source Software · doi:10.21105/joss.08532 · Cited by 1
MNIST Dataset Classification via Multiview Topological Data Analysis
Corona-Romero, K., Garcia-Ceja, E., & Mendoza-Montoya, O. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-09037-9_9 · Cited by 1
Refinement of interval approximations for fully commutative quivers
Hiraoka, Y., Nakashima, K., Obayashi, I., & Xu, C. (2025) · Japan Journal of Industrial and Applied Mathematics · doi:10.1007/s13160-025-00739-w · Cited by 1
“35 References 36 1 Introduction Topological data analysis (TDA) is a rapidly emerging field in applied mathematics that features leveraging algebraic topology tools to solve problems in data science [1].”
3d electron cloud descriptors for enhanced QSAR modeling of anti-colorectal cancer compounds
Li, J., Gu, R., Du, S., & Xu, L. (2025) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-025-00679-0 · Cited by 1
“…conditions, multi-dimensional feature integration can compensate for the limitations of individual descriptors, with well-constructed descriptor ensembles significantly enhancing QSAR model performance [152]. balance contributions from descriptors of differing dimen - sional nature [151].”
On interleaving distance as a complete metric for bifiltrations in Rn
Bihiye, Z. M., Brun, M., Mpimbo, M., & Mrema, E. (2025) · Research in Mathematics · doi:10.1080/27684830.2025.2573572 · Cited by 1
Esteve, M., Martinez-Gracia, A., Rodriguez-Sala, J. J., Brotons‐Mas, J. R., & Falcó, A. (2025) · Scientific Reports · doi:10.1038/s41598-025-23686-5 · Cited by 1
RetractedKoljančić, N., Park, S. A., Gurnari, D., et al. (2025) · Analytical Chemistry · doi:10.1021/acs.analchem.5c05342 · Cited by 1
PGTI: Pose-Graph Topological Integrity for Map Quality Assessment in SLAM
Xie, S., Sakurada, K., Ishikawa, R., Onishi, M., & Oishi, T. (2025) · Robotics · doi:10.3390/robotics14120189 · Cited by 1
“Topological Motivation: according to the persistent homology theory in Topological Data Analysis [43], global connectivity is recovered by growing ε -balls around samples and joining any pair whose balls overlap, forming a ˇCech/Vietoris–Rips complex.”
Insights in HIV-TB co-infection with TDA Mapper and machine learning
Ngoana, M. P., Netshifhire, R., Chirove, F., & Visaya, M. V. (2025) · Afrika Matematika · doi:10.1007/s13370-025-01402-w · Cited by 1
“TDA received notice and attention due to [ 17] critical work.”
On the evolutionary dynamics of complexity and consciousness
Edelman, S. (2026) · Frontiers in Complex Systems · doi:10.3389/fcpxs.2025.1672525 · Cited by 1
Bleile, Y. B., Yadav, P., Koehl, P., et al. (2026) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013890 · Cited by 1
“2 Theory: persistence homology applied to analysing cell contours”
Omrani, H., Omrani, H., Omrani, H., et al. (2026) · Next Chemical Engineering · doi:10.1016/j.nxcen.2026.100023 · Cited by 1
Hierarchical topological clustering and meaningful outliers
Carpio, A., & Duro, G. (2026) · Soft Computing · doi:10.1007/s00500-025-11028-6 · Cited by 1
“Highly persistent features span over a wide range of spatial scales, being more likely to represent true features of the data under study than to constitute artifacts of sampling, noise, or parameter choice [20]. a. Hierarchical topological clustering.”
Rojas, L. (2025) · Avances en Ciencia e Ingeniería · doi:10.65093/aci.v16.n4.2025.44 · Cited by 1
“Persistent homology is a central tool in TDA that captures the topological features of a point cloud or field at multiple scales (Carlsson, 2009).”
Manifold topological deep learning for biomedical data
Liu, X., Su, Z., Shi, Y., Tong, Y., Wang, G., & Wei, G. (2026) · Nature Communications · doi:10.1038/s41467-026-71392-1 · Cited by 5
A continuum support field theory with mass conservation for micro-scale structural rearrangement
Wang, J., & Ohsawa, Y. (2026) · AIP Advances · doi:10.1063/5.0315788 · Cited by 1
Computational mechanics empowered by artificial intelligence
Chinesta, F., Baranger, E., Jailin, C., Barasinski, A., Cinnella, P., & Feyel, F. (2026) · Mechanics & Industry · doi:10.1051/meca/2026022 · Cited by 2
Topological Characterization of Molecular Energy Landscapes Using Sublevel-Set Persistent Homology
Hernández, D. J., Cadavid, C. A., Luque, J., et al. (2026) · Mathematical and Computational Applications · doi:10.3390/mca31030108 · Cited by 1
“This discrete representation provides an approximation of the conformational space and enables the application of topological data analysis (TDA) techniques to investigate its underlying topological structure [12].”
Topological descriptors for learning-based, rotation-invariant protein surface classification
Guerra, M., Fugacci, U., & Biasotti, S. (2026) · Computers & Graphics · doi:10.1016/j.cag.2026.104688 · Cited by 1
Visualizing the effects of a changing distance on data using continuous embeddings
Gruenhage, G., Opper, M., & Barthelmé, S. (2016) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2016.06.006 · Cited by 1
“For complex data, distance or similarity can be arbitrary to some extent (Carlsson, 2009).”
BackgroundEstimating Multidimensional Persistent Homology Through a Finite Sampling
Cavazza, N., Ferri, M., & Landi, C. (2015) · International Journal of Computational Geometry & Applications · doi:10.1142/s0218195915500119 · Cited by 1
“Persistent Topology is an innovative way of matching topology and geometry, and it proves to be an effective mathematical tool in Pattern Recognition [18, 3], particularly in Shape Comparison [1].”
MethodComments on: Overview of object oriented data analysis
Rodríguez‐Casal, A. (2014) · Biometrical Journal · doi:10.1002/bimj.201300191 · Cited by 1
Betti numbers of multigraded modules of generic type
Charalambous, H., & Tchernev, A. (2014) · Journal of Pure and Applied Algebra · doi:10.1016/j.jpaa.2014.07.017 · Cited by 1
Marine Target Detection from Nonstationary Sea-Clutter Based On Topological Data Analysis
Nie, L., Jiang, W., & Fu, Q. (2012) · Procedia Engineering · doi:10.1016/j.proeng.2012.01.507 · Cited by 1
Incremental Delaunay Triangulation Construction for Clustering
Razafindramanana, O., Rayar, F., & Venturini, G. (2014) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2014.242 · Cited by 1
“Referring to Carlsson’s discussions [1], one very basic objective of data analysis is to construct qualitative geometric information out of quantitative information by means of analyzing pairwise distances among the data points in order to reveal connected components.”
BackgroundImproved rates for Wasserstein deconvolution with ordinary smooth error in dimension one
Dedecker, J., Fischer, A., & Michel, B. (2015) · Electronic Journal of Statistics · doi:10.1214/15-ejs997 · Cited by 1
“(TDA) [5], Wasserstein distances recently appeared to be natural metrics for controlling the estimation of geometric and topological features of the sampling measure and its support.”
Deconstructing Kernel Machines
Ali, M., Rushdi, M. A., & Ho, J. C. F. (2014) · Lecture notes in computer science · doi:10.1007/978-3-662-44848-9_3 · Cited by 1
“Perhaps deconstructive learning’s emphasis on the geometry of the decision boundary in high dimensional space and its connection with machine learning could provide stimulating examples or even counterexamples unbeknown to the geometers, and therefore, provide the needed motivation for the development of new type of h…”
BackgroundRelationship and Connectivity of Incomplete Data Collection
Chen, L. M. (2015) · Springer eBooks · doi:10.1007/978-3-319-25127-1_3 · Cited by 1
An Overview of Some Theoretical Topological Aspects of Big Data
Trovati, M. (2015) · Springer eBooks · doi:10.1007/978-3-319-25313-8_4 · Cited by 1
Big Data Models and the Public Sector
Sona, N. N. (2015) · Advances in public policy and administration (APPA) book series · doi:10.4018/978-1-4666-9649-5.ch014 · Cited by 1
Structural Knowledge Extraction from Mobility Data
Cottone, P., Gaglio, S., Re, G. L., Ortolani, M., & Pergola, G. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-49130-1_22 · Cited by 1
Catching homologies by geometric entropy
Felice, D., Franzosi, R., Mancini, S., et al. (2017) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2017.09.007 · Cited by 1
“So, we exploit algebraic topology tools in order to describe a network by means of its homology groups [15].”
MethodMetric Reconstruction Via Optimal Transport
Adamaszek, M., Adams, H., & Frick, F. (2018) · SIAM Journal on Applied Algebra and Geometry · doi:10.1137/17m1148025 · Cited by 1
“e compositions are homotopy equivalent to the corresponding identity maps by using linear homotopies. Motivation. We provide the following motivation for our work. In applications of topology to data [8], one is given a sampling of points X from an unknown underlying space M and would like to use X to recover informat…”
Shape of Educational Data: Interdisciplinary Perspectives
Ganley, C. M., & Hart, S. A. (2017) · Journal of Learning Analytics · doi:10.18608/jla.2017.42.2 · Cited by 1
“Hybrid topological numeric methods developed by Carlsson and others (Carlsson, 2009, 2012a, 2012b; Edelsbrunner & Harer, 2010; Lum et al., 2013) have revealed subgroups in data sets that traditional methodologies have failed to find.”
BackgroundMethodQuantitative Analysis of Range Image Patches by NEB Method
Wang, W. (2017) · MATEC Web of Conferences · doi:10.1051/matecconf/201713900024 · Cited by 1
Critical Points to Determine Persistence Homology
Asirimath, C., Ratnayake, J., Weeraddana, P. C., & Rajatheva, N. (2019) · Asilomar Conference on Signals, Systems and Computers (ACSSC) · doi:10.1109/ieeeconf44664.2019.9048780 · Cited by 1
“However, different features of the image may represent the images in a space that is more meaningful for a specified purpose [5]–[9].”
BackgroundGeometric goodness of fit measure to detect patterns in data point clouds
Hernández, A., & Solís, M. (2022) · Computational Statistics · doi:10.1007/s00180-022-01244-1 · Cited by 1
“2 Homology and Vietoris-Rips Complex The seminal work of Carlsson (2014) presents the ground basic definitions to define a homological structure for a data cloud of points.”
BackgroundAn Efficient Data Retrieval Parallel Reeb Graph Algorithm
Hajij, M., & Rosen, P. (2020) · Algorithms · doi:10.3390/a13100258 · Cited by 2
“Recent years have witnessed extensive research in topology-based methods to analyze and study data [1,2].”
BackgroundSinkhorn Divergence of Topological Signature Estimates for Time Series Classification
Stephen, C. (2018) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2018.00113 · Cited by 1
“Our method applies techniques from topological data analysis (TDA) in the form of persistent homology (PH) to characterize signals [6], [7].”
MethodTesting goodness of fit for point processes via topological data analysis
Biscio, C. A. N., Chenavier, N., Hirsch, C., et al. (2020) · Electronic Journal of Statistics · doi:10.1214/20-ejs1683 · Cited by 1
“Since the mathematical field of topology specializes in describing invariants of objects independently of the choice of a precise metric, these features are robust against small perturbations or different embeddings of the object [12, 13].”
BackgroundEfficient and Robust Persistent Homology for Measures
Buchet, M., Chazal, F., Oudot, S., et al. (2014) · ACM-SIAM Symposium on Discrete Algorithms (SODA) · doi:10.1137/1.9781611973730.13 · Cited by 1
Streamflow data analysis using persistent homology
Musa, S. M. S. S., Noorani, M. S. M., Razak, F. A., et al. (2019) · AIP conference proceedings · doi:10.1063/1.5111228 · Cited by 1
Field Choice Problem in Persistent Homology
Obayashi, I., & Yoshiwaki, M. (2023) · Discrete & Computational Geometry · doi:10.1007/s00454-023-00544-7 · Cited by 1
Joshi, M. A., & Joshi, D. (2020) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/810/1/012083 · Cited by 1
“Most of the conventional methods failed to capture the complex interaction between the various input variables, so we give an attempt to use TDA which is proven to be powerful way to capture connectivity information between the input parameters [2, 3].”
MethodTopological reconstruction of sub-cellular motion with Ensemble Kalman velocimetry
Yin, L., Sgouralis, I., & Maroulas, V. (2020) · Foundations of Data Science · doi:10.3934/fods.2020007 · Cited by 1
“The partitioning of R can be computationally achieved through construction of the appropriate topological nerve [12] via the Mapper algorithm [6, 46].”
MethodDescribing topology on the set of persistence diagrams
Zarichnyi, M. (2020) · Topology and its Applications · doi:10.1016/j.topol.2020.107409 · Cited by 1
Persistent homology in ℓ∞ metric
Beltramo, G., & Škraba, P. (2021) · Computational Geometry · doi:10.1016/j.comgeo.2021.101821 · Cited by 1
Improving Short-term Output Power Forecasting Using Topological Data Analysis and Machine Learning
Senekane, M., Matjelo, N. J., & Taele, B. M. (2021) · International Conference on Electrical, Computer and Energy Technologies (ICECET) · doi:10.1109/icecet52533.2021.9698599 · Cited by 1
Comparing Directed and Weighted Road Maps
Bittner, A., Fasy, B. T., Grudzien, M., Hajra, S. G., Huang, J., Pelatt, K. E., Thatcher, C. M., Tumurbaatar, A., & Wenk, C. (2018) · Association for Women in Mathematics series · doi:10.1007/978-3-319-89593-2_4 · Cited by 2
Collapsibility and homological properties of $$\mathfrak {I}$$-contractible transformations
Espinoza, J. F., Frias-Armenta, M.-E., & Hernández-Hernández, H. A. (2022) · Boletín de la Sociedad Matemática Mexicana · doi:10.1007/s40590-022-00434-7 · Cited by 1
Topological Data Analysis of Domain Pattern Formation in Materials
Mototake, Y.-I., Mizumaki, M., Kudo, K., et al. (2021) · Journal of Smart Processing · doi:10.7791/jspmee.10.108 · Cited by 1
Single-Cell Topological Simplicial Analysis Reveals Higher-Order Cellular Complexity
Lin, B. (2022) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm55620.2022.9995365 · Cited by 1
The Euler characteristic as a topological marker for outbreaks in vector-borne disease
de Souza, D. B., dos Santos, E. F., & Santos, F. A. N. (2022) · Journal of Statistical Mechanics Theory and Experiment · doi:10.1088/1742-5468/aca0e5 · Cited by 1
“One important set of tools used to explore and understand the shape of data is TDA [23, 67].”
Stable Topological Feature Vectors via Hermite Function Expansion on Persistence Curves
Chung, Y.-M. (2022) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata55660.2022.10020423 · Cited by 3
“Thus, by (7), the persistence curve of this example is P (D,ψ5(b, d))(x) = 1 11χ[1,2)(x) + 2 11χ[2,4)(x) + 2 11χ[2,4)(x) + 2 11χ[3,5)(x) + 4 11χ[1,5)(x).”
Chen, Z., Zhang, B., Gong, F., Wan, L., & Ma, L. (2023) · Frontiers in Genetics · doi:10.3389/fgene.2023.1110899 · Cited by 1
“Topological data analysis, which performs graph representation of high-dimensional datasets, provides another way to handle self-intersection structure (Carlsson, 2009).”
Topological measures of order for imperfect two-dimensional Bravais lattices
Shipman, P. D., Sharath, T., & Bradley, R. M. (2023) · Physical review. E · doi:10.1103/physreve.107.044216 · Cited by 1
Rapoport, D. L. (2023) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2482/1/012026 · Cited by 2
“The underlying notion is that data has shape (Carlsson [31,32], Rapoport [232-234], be that”
BackgroundMethodInfluentialBranco, S., Dogruluk, E., Carvalho, J. G., et al. (2023) · Computers · doi:10.3390/computers12060110 · Cited by 1
“It builds such shapes by grouping samples using a distance metric, where points with a distance of less than r from each other are considered connected [8,9].”
BackgroundIdentifying Critical Transition in Bitcoin Market Using Topological Data Analysis and Clustering
Bansal, A., Singh, A., Vats, S., et al. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-99-2322-9_7 · Cited by 1
Restrepo, M. (2023) · Studies in big data · doi:10.1007/978-3-031-38325-0_5 · Cited by 1
A Topological Data Analysis-Based Approach to Object Localization: A Comparison with ViT and Yolov7
Cheng, H., & Ong, Z. Z. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-47451-4_10 · Cited by 1
Multiscale Hodge scattering networks for data analysis
Saito, N., Schonsheck, S. C., & Shvarts, E. (2026) · Applied and Computational Harmonic Analysis · doi:10.1016/j.acha.2026.101876 · Cited by 1
Combinatorial methods for barcode analysis
Rodriguez, E. J. (2023) · Journal of Applied and Computational Topology · doi:10.1007/s41468-023-00143-8 · Cited by 1
“This theory has been applied to the study of data, where a filtration is applied to some dataset and its persistent homology groups are used to determine features in the underlying distribution [5, 10].”
MethodTime varying analysis of dynamic resting-state functional brain network to unfold memory function
Azizi, T. (2023) · Neuroscience Informatics · doi:10.1016/j.neuri.2023.100148 · Cited by 1
Significance of modes in the torus by topological data analysis
Yu, C., Jung, S., & Kim, J. (2023) · Stat · doi:10.1002/sta4.636 · Cited by 1
Generating High Dimensional Test Data for Topological Data Analysis
Singh, R. P., Malott, N. O., Sauerwein, B., Mcgrogan, N., & Wilsey, P. A. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-0316-6_2 · Cited by 2
Topological learning in multiclass data sets
Griffin, C. H., Karn, T., & Apple, B. (2024) · Physical review. E · doi:10.1103/physreve.109.024131 · Cited by 2
“Our approach is inspired by topological data analysis [35, 36], which provides methods for extracting topological information about a topological space T on which a data set X ⊆ R n resides.”
MethodKolodziej, M. A., & Waliszewski, P. (2024) · Advances in neurobiology · doi:10.1007/978-3-031-47606-8_9 · Cited by 1
Investigating rough single-fracture permeabilities with persistent homology
Fuchs, M., Suzuki, A., Hasumi, T., & Blum, P. (2024) · Solid Earth · doi:10.5194/se-15-353-2024 · Cited by 2
“Topological data analysis (TDA) is another way to extract crucial information of shapes and structures in big data (Carlsson, 2009; Thiele et al., 2016).”
MethodCovariance matrices of length power functionals of random geometric graphs – an asymptotic analysis
Reitzner, M., Römer, T., & von Westenholz, M. (2024) · Linear Algebra and its Applications · doi:10.1016/j.laa.2024.03.032 · Cited by 1
Detection of small holes by the scale-invariant robust density-aware distance (RDAD) filtration
Siu, C., Samorodnitsky, G. P., Yu, C. L., & Yao, A. (2024) · Journal of Applied and Computational Topology · doi:10.1007/s41468-024-00166-9 · Cited by 1
“Since the seminal work of [1], TDA has been used for a wide range of applications (for instance [2–7], see also the references therein).”
BackgroundIslambekov, U., Pathirana, H., Khormali, O., Akçora, C. G., & Smirnova, E. (2024) · Foundations of Data Science · doi:10.3934/fods.2024024 · Cited by 2
Ladder decomposition for morphisms of persistence modules
Urbančič, Ž., & Giansiracusa, J. (2024) · Journal of Applied and Computational Topology · doi:10.1007/s41468-024-00174-9 · Cited by 2
“For a thorough exposé on the theory of persistence, consult [11, 3].”
BackgroundInfluentialFrom samples to persistent stratified homotopy types
Mäder, T., & Waas, L. (2024) · Journal of Applied and Computational Topology · doi:10.1007/s41468-024-00170-z · Cited by 1
Persistent Path Homology for Quantitative Analysis of Carboranes
Wang, B., Zhang, M., & Pan, F. (2024) · Journal of Computational Biophysics and Chemistry · doi:10.1142/s2737416524500339 · Cited by 1
Persistent homology with k-nearest-neighbor filtrations reveals topological convergence of PageRank
Le, M., & Taylor, D. (2024) · Foundations of Data Science · doi:10.3934/fods.2024038 · Cited by 5
“TDA has been applied to numerous contexts ranging from visualization and dimensionality reduction [6, 23] and time series analyses [32] to applications in cosmology [37, 40], physical processes over networks [25, 38, 22, 21], neuroscience [33, 13, 8, 30], and systems biology [35, 26, 19, 18].”
MethodMay, N., Krishnamoorthy, B., & Gambill, P. (2024) · La Matematica · doi:10.1007/s44007-024-00130-0 · Cited by 1
Persistent homology of partially ordered spaces
Calk, C., Goubault, É., & Malbos, P. (2024) · Journal of Applied and Computational Topology · doi:10.1007/s41468-024-00190-9 · Cited by 1
“…Carlsson and Zomorodian 2009), as well as giving succinct descriptions of the semantics of concurrent and distributed systems, as some sort of barcodes (see e.g. Lesnick and Wright 2015), an approach which has proven very practical in different areas of engineering, see e.g. Carlsson (2009).”
InfluentialAl-Imran, M., Liza, M. Z. A., Shiraj, M. M. B., et al. (2024) · Journal of Computing Theories and Applications · doi:10.62411/jcta.11488 · Cited by 1
“Despite the potential of persistent homology, its use in image denoising remains relatively unexplored, with most research focusing on feature extraction and shape analysis [22] rather than noise reduction.”
BackgroundEmergent order in epithelial sheets by interplay of cell divisions and cell fate regulation
Greulich, P. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012465 · Cited by 1
“We start with model R, that is, setting If we choose as time unit the update scheme’s Monte Carlo time steps t ¼ 1 l þ o , the probability that a randomly selected site i with a B -cell becomes an A -cell after a Monte Carlo update is Similarly, we get the probability for an”
Quantum distance approximation for persistence diagrams
Ameneyro, B., Herrman, R., Siopsis, G., & Maroulas, V. (2024) · Journal of Physics Complexity · doi:10.1088/2632-072x/ad9fca · Cited by 1
“Any reader who wishes for more details on the subject may consult [40, 41, 51] for references on persistent homology and [48, 49, 52, 53] for QAOA references.”
Offroy, M., Duponchel, L., Razafitianamaharavo, A., Pagnout, C., & Duval, J. F. L. (2024) · Talanta · doi:10.1016/j.talanta.2024.127482 · Cited by 1
Multi-scale Hochschild spectral analysis on graph data
He, Y., & Liu, J. (2025) · AIMS Mathematics · doi:10.3934/math.2025064 · Cited by 1
A TDA-based performance analysis for neural networks with low-bit weights
Ogio, Y., Tsubone, N., Minami, Y., et al. (2025) · Artificial Life and Robotics · doi:10.1007/s10015-025-01005-5 · Cited by 1
“The basic idea of TDA [12–19] is to infer the structure of data in a scattered graph by measuring the gap between the radii of the birth and death of holes.”
Generalized Morse theory of distance functions to surfaces for persistent homology
Song, A., Yim, K. M., & Monod, A. (2025) · Advances in Applied Mathematics · doi:10.1016/j.aam.2025.102857 · Cited by 13
“Persistent homology is the leading algebraic method in topological data analysis that measures multiscale features in data and fundamentally depends on a distance function (Edelsbrunner & Harer, 2008; Ghrist, 2008; Carlsson, 2009).”
Balgi, S., Daoud, A., Peña, J. M., Wodtke, G. T., & Zhou, J. (2025) · Sociological Methods & Research · doi:10.1177/00491241251319291 · Cited by 4
Wang, R., Qiu, C., Hu, C.-S., Li, Z., & Wu, Y. (2025) · Physics Letters B · doi:10.1016/j.physletb.2025.139405 · Cited by 3
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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