The DOI matches its Crossref record; 77 of the 78 references it cites were found in a registry or an index; 15 of the 44 sources its claims cite were checked for support.
Topology and data
Open accessAbstract
An important feature of modern science and engineering is that data of various kinds is being produced at an unprecedented rate. This is so in part because of new experimental methods, and in part because of the increase in the availability of high powered computing technology. It is also clear that the nature of the data we are obtaining is significantly different. For example, it is now often the case that we are given data in the form of very long vectors, where all but a few of the coordinates turn out to be irrelevant to the questions of interest, and further that we don’t necessarily know which coordinates are the interesting ones. A related fact is that the data is often very high-dimensional, which severely restricts our ability to visualize it. The data obtained is also often much noisier than in the past and has more missing information (missing data). This is particularly so in the case of biological data, particularly high throughput data from microarray or other sources. Our ability to analyze this data, both in terms of quantity and the nature of the data, is clearly not keeping pace with the data being produced. In this paper, we will discuss how geometry and topology can be applied to make useful contributions to the analysis of various kinds of data. Geometry and topology are very natural tools to apply in this direction, since geometry can be regarded as the study of distance functions, and what one often works with are distance functions on large finite sets of data. The mathematical formalism which has been developed for incorporating geometric and topological techniques deals with point clouds, i.e. finite sets of points equipped with a distance function. It then adapts tools from the various branches of geometry to the study of point clouds. The point clouds are intended to be thought of as finite samples taken from a geometric object, perhaps with noise. Here are some of the key points which come up when applying these geometric methods to data analysis. • Qualitative information is needed: One important goal of data analysis is to allow the user to obtain knowledge about the data, i.e. to understand how it is organized on a large scale. For example, if we imagine that we are looking at a data set constructed somehow from diabetes patients, it would be important to develop the understanding that there are two types of the disease, namely the juvenile and adult onset forms. Once that is established, one of course wants to develop quantitative methods for distinguishing them, but the first insight about the distinct forms of the disease is key.
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Claims the paper makes
53 No basis given · 38 Own interpretation · 23 Own result · 17 No full text of the sources · 9 Supported · 5 Partly supported · 5 Sources not found · 1 Couldn’t confirm · 1 Not checked yet · accuracy not yet measured
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“Much of current research efforts are focused in this direction (see, e.g., [43] and [39]), and functoriality provides the right general mathematical framework for addressing them.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1. Introduction
What the sources say
- Could not verifycites [43] von Luxburg, Ulrike, 2008, Consistency of spectral clustering, Ann. Statist. sourceExperimental — accuracy not yet measured
The Introduction discusses questions about whether clustering algorithms converge, and Section 5.2 constructs operators for analyzing spectral clustering. Neither passage addresses functoriality or presents it as a framework for resolving ambiguities in clustering; searches for those concepts found no supporting passage in our copy. (LLM, conf=0.76)
- Not matchedcites [39] kleinbergJ.M. Kleinberg, An impossibility theorem for clustering, NIPS 2002: 446-453.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: unclear — It is unclear whether it could be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Could not verify
Every source the paper cites for this claim was checked and supports it.
In the paper
“We argue that, as suggested in [46], it has a role to play in the study of point cloud data as well, and we give two illustrations of how this could happen, within the context of clustering.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1. Introduction
What the sources say
- Supportedcites [46] McCullagh, Peter, 2002, What is a statistical model?, Ann. Statist. source
Section 8, “Extensive response variable” (page unavailable), describes a point process as a countable set of points and treats Poisson point-process models as natural transformations of functors. That supports the cited suggestion that functoriality could be useful for point-cloud data; the citing authors present clustering as their own proposed application. (LLM, conf=0.86)
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Niyogi, Smale, and Weinberger in [52] provide sufficient hypotheses which guarantee that this is possible for Riemannian manifolds.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.3. Persistent homology.
What the sources say
- Partly supportedcites [52] Niyogi, Partha, 2008, Finding the homology of submanifolds with high confidence from random samples, Discrete Comput. Geom. sourceExperimental — accuracy not yet measured
Section 2 (Preliminaries; page unavailable) considers compact Riemannian submanifolds sampled as data. Section 3 (An Outline of Our Main Results; page unavailable) gives conditions under which the samples recover the manifold’s homology with high confidence. It does not guarantee reconstruction of the manifold itself, so the claim is broader than the result. (LLM, conf=0.88)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“The result of this construction is a database M of ca.4.5 × 10 6 points on a 7-dimensional ellipsoid in R 8 .”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.4. Example: Natural image statistics.
What the sources say
- Partly supportedcites [41] lee A.B. Lee, K.S. Pedersen, and D. Mumford, The nonlinear statistics of high-contrast patches in natural images, International Journal of Computer Vision (54), No. 1-3, August 2003, pp. 83-103. sourceExperimental — accuracy not yet measured
Section 3 (Preprocessing; page unavailable) says the preprocessed points lie on a 7-dimensional ellipsoid in R⁹, which is transformed into a 7-dimensional sphere in R⁸. Section 2 (Optical and Range Data Sets; page unavailable) reports about 4.2 million optical patches, not approximately 4.5 million points in a database M. The source supports the general dimensional construction, but not the stated count and geometry together. (LLM, conf=0.85)
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Their method is to prove that the Čech complex associated to a covering by balls of a fixed radius is homotopy equivalent to the underlying manifold.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.3. Persistent homology.
What the sources say
- Supportedcites [52] Niyogi, Partha, 2008, Finding the homology of submanifolds with high confidence from random samples, Discrete Comput. Geom. source
Section 3 states that, when the sample is sufficiently dense and the ball radius meets the stated bound, the manifold is a deformation retract of the union of the balls. Section 3.1 constructs the nerve of that ball cover and invokes the Nerve Lemma because the balls are convex. Together, these support the claimed homotopy equivalence under the method’s conditions; the paper calls the nerve a simplicial complex rather than a Čech complex. (LLM, conf=0.88)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“However, it turns out that there is a classification theorem (see [64]) for a subcategory of the category of N-persistence F -vector spaces, where F is a field.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1=2
What the sources say
- Supportedcites [64] Zomorodian, Afra, 2005, Computing persistent homology, Discrete Comput. Geom. source
Section 3.1 (p. 11) establishes an equivalence between finite-type persistence modules and finitely generated graded modules over R[t]. Corollary 3.1 in Section 3.2 (p. 12) then classifies the isomorphism classes of finite-type persistence modules over a field F by finite sets of P-intervals. Finite-type modules are the subcategory referred to in the claim. (LLM, conf=0.94)
plain- Asserted: passes — The authors put it forward as true.
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- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“In [9], such a context is developed which is not dependent on the choice of a cost function but is rather dependent on”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
What the sources say
- Supportedcites [9] memolicarlsson G. Carlsson and F. Memoli, Persistent Clustering and a Theorem of J. Kleinberg , Preprint, March 2008. source
The Discussion (p. 16) presents persistence and functoriality as organizing principles and proposes defining clustering methods through sets of constraints, rather than a chosen cost function. The Results (p. 10) explicitly compares the framework’s properties with Kleinberg’s axiomatic conditions. (LLM, conf=0.87)
plain- Asserted: passes — The authors put it forward as true.
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- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“It can be shown that p Σ is a homotopy equivalence (see [65]).”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 4.3. Tree based persistence.
What the sources say
- Supportedcites [65] Zomorodian, Afra, 2008, Localized homology, Comput. Geom. source
Lemma 1 (global; page unavailable) states that projection from a blowup complex to the covered space is a homotopy equivalence when a simplicial complex is covered by subcomplexes. The paper uses π_X and X_U rather than pΣ and M(|Σ|,S), but states the projection result attributed to it. (LLM, conf=0.88)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“This notion of clustering is closely related to clique clustering algorithms in network clustering [53].”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
What the sources say
- Supportedcites [53] clique G. Palla, I. Derènyi, I. Farkas, and T. Vicsek, Uncovering the overlapping community structure of complex networks in nature and society, Nature, Volume 435, 9 June 2005, pp. 814-818. source
The paper defines network communities through connected sets of adjacent k-cliques (p. 2) and describes an algorithm that finds cliques and clusters them using a clique-overlap matrix (p. 3). It therefore supports the citation as an example of clique-based network clustering; the comparison to the citing paper’s equivalence relation E is the citing authors’ own framing. (LLM, conf=0.88)
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“There is a unique M gen -functorial persistent clustering algorithm Ξ which is scale invariant and which satisfies the requirement that Ξ(E) = P .”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
What the sources say
- Partly supportedcites [9] memolicarlsson G. Carlsson and F. Memoli, Persistent Clustering and a Theorem of J. Kleinberg , Preprint, March 2008. sourceExperimental — accuracy not yet measured
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
- It may state two separate claims as one. 39
- Probably not a substantial claim. 41
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Although clustering is clearly a very important part of data analysis, the ways in which it is formulated and implemented are fraught with ambiguities.In particular, the arbitrariness of various threshhold choices and lack of robustness are difficulties one confronts.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1. Introduction
What the sources say
- Partly supportedcites [43] von Luxburg, Ulrike, 2008, Consistency of spectral clustering, Ann. Statist. sourceExperimental — accuracy not yet measured
The Introduction describes clustering as widely used and says its consistency guarantees are unsatisfactory. The discussion following Proposition 24 shows that unnormalized spectral clustering can fail and that its reliability can be difficult to assess. These passages support the general concern about reliability, but they do not identify arbitrary threshold choices as a difficulty or establish a lack of robustness for clustering generally; no page numbers are available in our copy. (LLM, conf=0.82)
- Not matchedcites [39] kleinbergJ.M. Kleinberg, An impossibility theorem for clustering, NIPS 2002: 446-453.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“In particular, in [41], indications were found that the data was concentrated around an annulus.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.4. Example: Natural image statistics.
What the sources say
- Supportedcites [41] lee A.B. Lee, K.S. Pedersen, and D. Mumford, The nonlinear statistics of high-contrast patches in natural images, International Journal of Computer Vision (54), No. 1-3, August 2003, pp. 83-103. source
Section 5.1 describes the ideal-edge manifold using a circular orientation parameter and a bounded displacement parameter, giving it an annulus-like parametrization. Section 5.2 finds evidence that optical data points concentrate near that manifold. The paper does not use the word “annulus,” but the citing sentence is a reasonable, hedged paraphrase. (LLM, conf=0.82)
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“By using persistence, one is able to develop a systematic way of addressing such questions.That methodology has been described in [65].”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 4.3. Tree based persistence.
What the sources say
- Supportedcites [65] Zomorodian, Afra, 2008, Localized homology, Comput. Geom. source
Section 1.2 describes a method for localizing topological attributes relative to a cover. Section 4.4 gives the persistence algorithm that computes representatives of homology classes, supplying descriptions of those attributes. Together, these passages support the paraphrase about systematically addressing where features come from. (LLM, conf=0.90)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“The algorithm Ξ is the algorithm which associates to a finite metric space (X, d X ) the persistent set {π 0 (V R(X, ))} ≥0 .”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
What the sources say
- Supportedcites [9] memolicarlsson G. Carlsson and F. Memoli, Persistent Clustering and a Theorem of J. Kleinberg , Preprint, March 2008. source
Example 2.1 (Section 2, p. 3) associates a finite metric space with a persistent set whose blocks at scale r contain exactly the points connected by chains of distances at most r. Those blocks are the connected components of the Vietoris–Rips complex at that scale, as the claim describes. (LLM, conf=0.90)
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Also, results about barcodes under the general heading have now begun to appear (see [15] and [16]).”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Additional information
What the sources say
- Supportedcites [15] Cohen-Steiner, David, 2007, Stability of persistence diagrams, Discrete Comput. Geom. source
The Introduction (p. 2) says the paper makes concrete statements about the stability of barcodes. The Applications section (p. 16) states a Barcode Theorem, making this paper an example of the barcode results the citing sentence says have begun to appear. (LLM, conf=0.90)
- Not matchedcites [16] stabilitytwo D. Cohen-Steiner, H. Edelsbrunner, J. Harer and Y. Mileyko, Lipschitz functions have 𝐿_{𝑝}-stable persistence, Found. Comput. Math., to appear.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Supported
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“It is readily checked that θ is a functor from N pers (Ab) to the category of graded Z[t]-modules, and is in fact an equivalence of categories, since an inverse functor can be given by M * → {M n }, where the morphisms ψ mn are given by multiplication by t n-m .”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1=2
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A first observation is that the points are scattered throughout the 7-sphere, in the sense that no point on S 7 is very far from the set, but that the density appears to vary a great deal.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.4. Example: Natural image statistics.
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We note that there are a number of short lines, and one long one.According to the philosophy mentioned above, this suggests that the first Betti number should be estimated to be one.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.4. Example: Natural image statistics.
hedgedcorrelationalSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We note that in this case, there are many short segments and 5 longer segments.It is again the case that this barcode is stable over the choices of landmark points and samples from M. This suggests that the first Betti number of the putative underlying space should be five.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Primary circle
hedgedcorrelationalSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Note that while the two smaller holes have now been “filled in”, a new hole has been introduced in the lower right-hand portion of the figure.Consequently, if we were to compute the homology of this complex, we would obtain a first Betti number of two.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1=2
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“We note that the vertical circles intersect the horizontal primary circle in two points, and do not intersect each other, so the picture in question produces a natural embedding of the three circle model in the Klein bottle.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Three circle model in the data
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We note that the β 0 barcode (the upper picture) clearly shows the single component, the β 1 shows two lines from threshhold parameter values .15 to .35, and finally the β 2 barcode shows a single line on roughly the same interval.This gives β 0 = 1, β i = 2, and β 2 = 1.These are the mod 2 Betti numbers for the Klein bottle.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Three circle model in the data
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“At both scales, there is a central dense core, and two”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 3.5. Examples.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These two are distinguished by mod 3 homology, and we have performed the computation to show that the mod 3 homology is consistent with the Klein bottle and not with the torus.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Three circle model in the data
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We note that given only the Mapper output, one might suspect that the small feature (the array of orange nodes) could simply be an artifact, but examination of the data shows that they correspond to essentially distinct contact maps.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 3.5. Examples.
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Some experimentation with the three circle model results in the following picture in which the horizontal segments form the primary circle and the vertical segments form the secondary circles, as in the diagram.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Three circle model in the data
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We have informally confirmed that the indicated patches are the ones which occur in the high density portions of the data set.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Three circle model in the data
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“By far the most frequently occurring signatures were (1, 1, 0) and (1, 0, 1), corresponding to a circle and a sphere, respectively.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.5. Example: Electrode array data from primary visual cortex.
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Note that the two secondary circles each intersect the primary circle in two points, and that they do not intersect each other.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Three circle model in the data
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Note also that the data are obtained by simply examining the states occurring in the simulation, and that it does not include any dynamic information which would show how the states are traversed in the folding process.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 3.5. Examples.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The set S exhibits the three circle model clearly, but enlarging it still does not exhibit the non-trivial β 2 which would be characteristic of the Klein bottle.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Three circle model in the data
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“It is easy to check (a) that the factorization is a homeomorphism and (b) that the orbit space is homeomorphic to a Klein bottle.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Three circle model in the data
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“A M iso -functorial clustering algorithm determines an choice p ι ∈ P G ι ι for every ι, and conversely an arbitrary choice of such p ι 's determines an M iso -functorial clustering algorithm.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Each of these notions creates the structure of a category whose objects are the finite metric spaces, since one can readily observe that each of the classes of morphisms is closed under composition and contains the identity.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Some work in clustering theory has been done in trying to determine the optimal choice of , but it is now well understood that it is much more informative to maintain the entire dendrogram of the set, which provides a summary of the behavior of clustering under all possible values of the parameter at once.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1. Introduction
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In the biological context, notions of distance are constructed using some intuitively attractive measures of similarity (such as BLAST scores or their relatives), but it is far from clear how much significance to attach to the actual distances, particularly at large scales.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1. Introduction
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Topology studies only properties of geometric objects which do not depend on the chosen coordinates, but rather on intrinsic geometric properties of the objects.As such, it is coordinate-free.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1. Introduction
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Moreover, it is understood that most of the information about topological spaces can be obtained through diagrams of discrete sets, via a process of simplicial approximation.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1. Introduction
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We then demonstrate that persistence can be generalized in several different directions, providing more structure and information about the data sets in question.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Example.
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“If the clusterings are consistent, i.e. if the clusters in C(X 1 ) and C(X 2 ) in C(X 1 ∪ X 2 ) correspond well under these maps, then one can regard that as evidence that the subsample clusterings actually correspond to clusterings on the full data set X.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Example.
hedgedcorrelationaln = 2- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“For any topological space X with a finite number of path components, β 0 (X) is the number of path components.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Example.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“We begin with a discussion of persistent homology, which is a mathematical formalism which permits us to infer topological information from a sample of a geometric object, and we show how it can be applied to particular data sets arising from natural image statistics and neuroscience.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Example.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Functoriality is central in algebraic topology in that the functoriality of homological invariants is what permits one to compute them from local information, and that functoriality is at the heart of most of the interesting applications within mathematics.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1. Introduction
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We then show that the philosophy of functoriality can be used to reason about the nature of clustering methods, and we conclude by speculating about theorems one might hope to prove and discussing how the subject might develop more generally.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Example.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“One important goal of data analysis is to allow the user to obtain knowledge about the data, i.e. to understand how it is organized on a large scale.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1. Introduction
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Although we often receive data in the form of vectors of real numbers, it is frequently the case that the coordinates, like the metrics mentioned above, are not natural in any sense, and that therefore we should not restrict ourselves to studying properties of the data which depend on any particular choice of coordinates.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1. Introduction
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The second method is more efficient, since it is precisely adapted to the complex at hand.Furthermore, it contains complete information about the original R-vector space.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1=2
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In interpreting the output, one now finds that long intervals in the output barcode indicate the presence of a homology class which “persists” over a long range of parameter values, while short intervals indicate cycles which are “born” at a given parameter value and then “die” at a nearby parameter value.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1=2
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“This is due to the fact that for finite metric spaces, it is generically the case that each value of the distance is taken only for one pair of points, so one does not have any points which are equidistant between a pair of landmarks.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.2. Building coverings and complexes.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Intuitively, we regard these two holes as coming from faulty sampling or other errors in the recovery of the data.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1=3
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The pictures and discussion above allow us to formulate the intuition that long intervals correspond to large scale geometric features in the space and that short intervals correspond to noise or inadequate sampling.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1=2
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Both the the Čech and Vietoris-Rips complexes typically produce simplices in dimensions much higher than the dimension of the space.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.2. Building coverings and complexes.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“It turns out that H simp k (X, Z) is always canonically isomorphic to the singular homology of the space |X|.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Remark.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The conclusion is that for simplicial complexes, homology is algorithmically computable, although standard methods for computing Smith normal form can often be painfully slow.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Remark.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“If further is sufficiently small, then all the balls will be geodesically convex, and the complex will compute the homology of M correctly.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.3. Persistent homology.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Note that if the centers actually lie on a submanifold M ⊆ R n , and the set X is sufficiently dense in M , then this complex is the Čech complex attached to a covering of M .”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.3. Persistent homology.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“It is clearly the case that if we have 0 ≤ ≤ , then we have an inclusion W s (X, L, ) ↩→ W s (X, L, ) and similarly for W s V R , W w , and W w V R .”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.2. Building coverings and complexes.
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, for finite metric spaces, it generically produces degenerate (i.e.discrete) complexes, with no one dimensional simplices.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.2. Building coverings and complexes.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In rough terms, δ k for large values of k computes density using points in large neighborhoods of x, and for small values uses small neighborhoods.So, δ k for large k corresponds to a smoothed out notion of density, and for small k corresponds to a version which carries more of the detailed structure of the data set.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.4. Example: Natural image statistics.
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“It was observed in [38] that in an informal sense, the images in the different conditions appeared to be quite similar, and a statistical analysis strongly suggested that the behavior of V1 in the spontaneous condition was consistent with a behavior which consisted in moving through a family of evoked images corresponding to responses to angular boundaries, without any particular order.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.5. Example: Electrode array data from primary visual cortex.
What the sources say
- Not checked — no full text of the sourcecites [38] kenet T. Kenet, D. Bibitchkov, M. Tsodyks, A. Grinvald, and A. Arieli, Spontaneously emerging cortical representations of visual attributes, Nature 425 (2003), 954-956. source
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“This result is consistent with what was obtained by using ISOMAP in [61].”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 3.5. Examples.
What the sources say
- Not checked — no full text of the sourcecites [61] isomapJ.B. Tenenbaum, V. de Silva and J.C. Langford, A global geometric framework for nonlinear dimensionality reduction, Science 290 (2000) (December), pp. 2319-2323. source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The arrays of neurons studied in [59] are from the primary visual cortex or V1 in Macaque monkeys.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.5. Example: Electrode array data from primary visual cortex.
What the sources say
- Not checked — no full text of the sourcecites [59] visionG. Singh, F. Memoli, T. Ishkhanov, G. Carlsson, G. Sapiro and D. Ringach, Topological Structure of Population Activity in Primary Visual Cortex, Journal of Vision, Volume 8, Number 8, Article 11, pp. 1-18, 2008. source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“since the intuition is that features which appear at several levels in such a multiresolution diagram would be more intrinsic to the data set than those which appear at a single level.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Definition 3.2.
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The core consists of normal or near-normal patients, and the two flares consist of patients with the two different forms of diabetes.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 3.5. Examples.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Z[m, n]-persistence vector spaces are examples of quiver representations, a highly developed area of algebra.See [29] for a complete description.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Corollary 4.6.
What the sources say
- Not checked — no full text of the sourcecites [29] Gabriel, P., 1997 source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These functions are clearly invariants of the isomorphism class of the module M , and we refer to them as the dimension and rank invariants, respectively.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Definition 4.3.
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“If the question is instead whether or not an element arises from a union of k elements of S, then we do not obtain complete information this way, but we do obtain partial information in that we can preclude the possibility that a class arises from such a union.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 4.3. Tree based persistence.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“We note that the correspondence X → π 0 (X) can actually be viewed as a functor (see [44]) from the category of topological spaces to the category of sets, in the sense that a continuous map f : X → Y induces a map of sets π 0 (f ) : π 0 (X) → π 0 (Y )”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
What the sources say
- Not matchedcites [44] Mac Lane, Saunders, 1998
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As we have seen above, algebraic topology is capable of producing signatures which indicate the presence of topological features within a space.As it stands, however, it is not capable of describing the source of the feature, i.e.where in the space the hole or other feature is located.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 4.3. Tree based persistence.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Theorem 4.5.Let M denote any Z[m, n]-persistent F -vector space, so that every vector space V i is finite dimensional.Then there is an isomorphism”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Example.
What the sources say
- Not checked — no full text of the sourcecites [29] Gabriel, P., 1997 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“As Kleinberg points out, it is possible to do so in a number of different ways by specifying cost functions on particular clusterings, and prove a uniqueness result for optimal choices of clusterings with respect to this cost function”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
What the sources say
- Not matchedcites [39] kleinbergJ.M. Kleinberg, An impossibility theorem for clustering, NIPS 2002: 446-453.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“This machinery is not available in the multivariable case, but it turns out that it can be replaced by the Gröbner basis methodology (see [18] or [47]).”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Definition 4.3.
What the sources say
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Any finite Z-persistence F -vector space M can be decomposed as”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Corollary 4.6.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The properties of trees guarantee that ≤ T is a partial ordering.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 4.3. Tree based persistence.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
In the paper
“In the paper [59], this was shown by simulation, which is of course always an option.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
What the sources say
- Not checked — no full text of the sourcecites [59] visionG. Singh, F. Memoli, T. Ishkhanov, G. Carlsson, G. Sapiro and D. Ringach, Topological Structure of Population Activity in Primary Visual Cortex, Journal of Vision, Volume 8, Number 8, Article 11, pp. 1-18, 2008. source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Not only is the correspondence π 0 (-) a functor from spaces to sets, it is equipped with a natural surjective map of sets η X : X → π 0 (X).”
- Also stated in: § 5. Reasoning about clustering · plain
“Further, these surjective maps have the property that for every continuous map f : X → Y , the diagram of sets (below) commutes.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
- Also stated in: § 5. Reasoning about clustering · plain
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“While the observation is of course suggestive, to prove that it indicates structure in the data set one must show that segments of the indicated length cannot occur in a random model associated to a null hypothesis.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This means that Kleinberg's conditions also make sense in this context.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
In the paper
“We point out that in the context of ordinary homology (i.e.not persistent homology), M. Penrose and others have developed a theory of “geometric random graphs” and have proved various results concerning the Betti numbers of complexes attached to such graphs (see [54]).”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Additional information
What the sources say
- Not checked — no full text of the sourcecites [54] Penrose, Mathew, 2003 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It is clear that a C-functorial clustering algorithm is a clustering algorithm in the sense of Kleinberg, since the surjective map of sets from X to χ(X, d X ) yields a partition of X, namely the partition whose blocks are the sets η -1 X (z), as z ranges over all elements of χ(X, d X ).”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
None of the sources cited for this claim have been checked yet.
In the paper
“Each such interval can be considered as a point in the set D = {(x, y) ∈ R 2 |x ≤ y}, and the output is therefore a finite subset of the set D. As such, we now have a finite spatial point process (see [20], [3]).”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Additional information
What the sources say
- Not matchedcites [20] Daley, D. J., 1988
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked — no full text of the sourcecites [3] Baddeley, Adrian, 2007, Spatial point processes and their applications source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
The paper relies on this without support.
There is no classification theorem for R-persistence abelian groups.
“However, we do not have such a theorem.”
The paper relies on this without support.
There is still no classification theorem for graded Z[t]-modules.
“Now, there is still no classification theorem for graded Z[t]-modules.”
The paper relies on this without support.
In studying data from experiments, one typically cannot assume that the data lies along a submanifold.
“If one is interested in studying data from experiments, though, one typically cannot assume that the data lies along a submanifold.”
The paper relies on this without support.
Even if one could assume that the data lies along a manifold, one is usually not in a position to verify that the stringent hypotheses of [52] are satisfied.
“Further, even if one could assume that the data lies along a manifold, one is usually not in a position to verify that the stringent hypotheses of [52] are satisfied.”
The paper relies on this without support.
The analysis of populations of neurons is amenable to geometric analysis.
“The second problem appears to be very amenable to geometric analysis, since it will involve the activities of several neurons at once.”
The paper relies on this without support.
The mechanism by which V1 carries out low-level tasks such as edge and line detection is not understood.
“However, the mechanism by which it carries out these tasks is not understood.”
The paper relies on this without support.
The classification of finitely generated modules over multivariable polynomial rings is much more complicated than the corresponding result for single-variable polynomial rings, and no reasonable parametrization is known.
“As is well known to algebraists, the classification of finitely generated modules over multivariable polynomial rings is much more complicated than the corresponding result for single variable polynomial rings, and in fact no reasonable parametrization is known.”
The paper relies on this without support.
In the case of more than one variable, the classification of multigraded modules depends on the field in question.
“examples show that in the case of more than one variable, the classification of multigraded modules definitely depends on the field in question.”
The paper relies on this without support.
The classification in the multivariable case is parametrized by points in moduli varieties over the ground field.
“In fact, the classification in the multivariable case is parametrized by points in moduli varieties over the ground field”
The paper relies on this without support.
Hierarchical clustering algorithms do not report single partitions but rather dendrograms, which are roughly speaking R+-persistent sets.
“This is not an unreasonable thing to do in view of the fact that hierarchical clustering algorithms do not report single partitions but rather dendrograms, which are roughly speaking R + -persistent sets.”
Citations
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Cited by
Sorted by citations
Items 1,201–1,300 of 2,331
Interactions Between Kernels, Frames, and Persistent Homology
Guillemard, M., & Iske, A. (2017) · Applied and numerical harmonic analysis · doi:10.1007/978-3-319-55556-0_14 · Cited by 5
Predicting position along a looping immune response trajectory
Rath, P., Allen, J. A., & Schneider, D. S. (2018) · PLoS ONE · doi:10.1371/journal.pone.0200147 · Cited by 4
Haze detection using persistent homology
Zulkepli, N. F. S., Noorani, M. S. M., Razak, F. A., et al. (2019) · AIP conference proceedings · doi:10.1063/1.5111219 · Cited by 4
Fast and Scalable Complex Network Descriptor Using PageRank and Persistent Homology
Hajij, M., Munch, E., & Rosen, P. (2020) · International Conference on Intelligent Data Science Technologies and Applications (IDSTA) · doi:10.1109/idsta50958.2020.9264141 · Cited by 4
“Persistent homology provides a robust set of tools for the theoretical and practical capacity to understand the shape of data [4] in any number of dimensions and on multiple scales, placing the concept of shape, as applied to data analysis, on a solid mathematical foundation.”
BackgroundCalcina, S. G. S., & Gameiro, M. (2021) · Mathematics and Computers in Simulation · doi:10.1016/j.matcom.2021.01.013 · Cited by 4
Persistent Topology of Protein Space
Hamilton, W., Borgert, J. E., Hamelryck, T., & Marron, J. S. (2022) · Association for Women in Mathematics series · doi:10.1007/978-3-030-95519-9_10 · Cited by 5
Homotopy continuation for the spectra of persistent Laplacians
Wei, X., & Wei, G. (2021) · Foundations of Data Science · doi:10.3934/fods.2021017 · Cited by 4
“Persistent homology has been proved to be a powerful tool in analyzing the geometry shape and topological persistence of data [8, 10].”
MethodMorse inequalities for the Koszul complex of multi-persistence
Guidolin, A., & Landi, C. (2023) · Journal of Pure and Applied Algebra · doi:10.1016/j.jpaa.2023.107319 · Cited by 4
Persistent homology and the shape of evolutionary games
Stenseke, J. (2021) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2021.110903 · Cited by 4
“The basic motivation is that shape matters, and features that persist over a wide range of spatial scales are assumed to represent real higher-order features of the underlying space (Carlsson, 2009).”
BackgroundNeural evidence for image quality perception based on algebraic topology
Liu, C., Yu, D., Ma, X., Xie, S., & Zhang, H. (2021) · PLoS ONE · doi:10.1371/journal.pone.0261223 · Cited by 4
Variably Scaled Persistence Kernels (VSPKs) for persistent homology applications
Marchı, S. D., Lot, F., Marchetti, F., et al. (2022) · Journal of Computational Mathematics and Data Science · doi:10.1016/j.jcmds.2022.100050 · Cited by 4
Visibility Graphs, Persistent Homology, and Rolling Element Bearing Fault Detection
Lee, D., & Sabater, A. B. (2022) · IEEE Conference on Prognostics and Health Management · doi:10.1109/icphm53196.2022.9815768 · Cited by 4
Zhen, Z., Chen, Y., Segovia-Domínguez, I., et al. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-05936-0_40 · Cited by 4
An overview to analyticity of dual functions
Durmaz, O., Aktaş, B., & Keçilioğlu, O. (2022) · Communications Faculty Of Science University of Ankara Series A1Mathematics and Statistics · doi:10.31801/cfsuasmas.1035344 · Cited by 4
Topological Data Analysis for Electric Motor Eccentricity Fault Detection
Wang, B., Lin, C., Inoué, H., & Kanemaru, M. (2022) · Annual Conference of the IEEE Industrial Electronics Society (IECON) · doi:10.1109/iecon49645.2022.9968912 · Cited by 6
“More rigorous definitions and detailed descriptions on persistent homology can be found in several references [12]–[14].”
BackgroundNeighborhood hypergraph model for topological data analysis
Liu, J., Chen, D., Li, J., & Wu, J. (2022) · Computational and Mathematical Biophysics · doi:10.1515/cmb-2022-0142 · Cited by 4
Persistence of Weighted Ordinal Partition Networks for Dynamic State Detection
Myers, A., Khasawneh, F. A., & Munch, E. (2023) · SIAM Journal on Applied Dynamical Systems · doi:10.1137/22m1476848 · Cited by 4
“There are now many examples where persistent homology [32], the flagship tool from TDA, can be used to measure the structure of a reconstructed attractor.”
MethodInfluentialTopological data analysis of noise: Uniform unimodal distributions
Gluzberg, V. E., & Katz, Y. A. (2023) · Communications in Nonlinear Science and Numerical Simulation · doi:10.1016/j.cnsns.2023.107216 · Cited by 4
Topological Data Analysis in Automotive Industry
Beutenmüller, F., Dierolf, B., Keckeisen, M., Pausinger, F., & Vaudrevange, P. K. S. (2023) · Proceedings · doi:10.1007/978-3-658-42048-2_4 · Cited by 4
Distributing Persistent Homology via Spectral Sequences
Torras-Casas, Á. (2023) · Discrete & Computational Geometry · doi:10.1007/s00454-023-00549-2 · Cited by 4
“uch as the study of geometric structure of sets of points lying in Rn, see [15,18]. This introduced the field of Topological Data Analysis which, very soon, was applied to a multitude of problems, see [6,19] for a survey article and an introduction. Among others, persistent homology has been applied to study coverage i…”
BackgroundA topological classifier to characterize brain states: When shape matters more than variance
Ferrà, A., Cecchini, G., Fisas, F.-P. N., Casacuberta, C., & Cos, I. (2023) · PLoS ONE · doi:10.1371/journal.pone.0292049 · Cited by 4
“Its main tool is persistent homology [33, 34, 35, 36], an algebraic characteristic of simplicial complexes equipped with a real-valued filtering function.”
Persistent cup product structures and related invariants
Mémoli, F., Stefanou, A., & Zhou, L. (2023) · Journal of Applied and Computational Topology · doi:10.1007/s41468-023-00138-5 · Cited by 4
“In Topological Data Analysis (TDA), one studies the evolution of homology across a filtration of spaces, called persistent homology [16, 17, 22, 32, 36, 37, 59, 66].”
BackgroundComputing Geometric Feature Sizes for Algebraic Manifolds
Rocco, S. D., Edwards, P. B., Eklund, D., Gäfvert, O., & Hauenstein, J. D. (2023) · SIAM Journal on Applied Algebra and Geometry · doi:10.1137/22m1522656 · Cited by 4
Biwer, C., Rothberg, A. E., IglayReger, H. B., Derksen, H., Burant, C., & Najarian, K. (2017) · PLoS ONE · doi:10.1371/journal.pone.0177696 · Cited by 4
Topological data analysis and applications
Costa, J. P. (2017) · International Convention on Information and Communication Technology, Electronics and Microelectronics · doi:10.23919/mipro.2017.7973488 · Cited by 4
“Encoding persistent topological features of a pointcloud (on the left) into a persistence diagram (on the right) by approximating the space through simplicial complexes (in the middle) [4].”
BackgroundWubie, B. A., Andrès, A., Greiner, R., Hoehn, B., Montaño‐Loza, A. J., Kneteman, N. M., & Heo, G. (2018) · Association for Women in Mathematics series · doi:10.1007/978-3-319-89593-2_9 · Cited by 4
Signature of Topologically Persistent Points for 3D Point Cloud Description
Beksi, W. J., & Papanikolopoulos, N. P. (2018) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2018.8460605 · Cited by 4
“Persistent homology is a particularly useful tool in TDA [10].”
BackgroundInfluentialAlgorithms for Topological Analysis of Spatial Data
Eremeev, S., & Seltsova, E. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-12082-5_8 · Cited by 4
Estimating Betti Numbers Using Deep Learning
Paul, R., & Chalup, S. K. (2019) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2019.8852277 · Cited by 4
“, by a filtration of simplicial complexes [7], [8], [14].”
BackgroundMethodDiscrete Morse theory, persistent homology and Forman–Ricci curvature
Saucan, E. (2021) · Mathematics Computation and Geometry of Data · doi:10.4310/mcgd.2021.v1.n2.a1 · Cited by 4
“The basic stimulus is, to be sure, the ever growing importance in a variety of applied fields of the Persistent Homology [16], [9], [10], [11], [42], not the least among them being the intelligence of Complex Networks [22], [30], [29] – to name just a few among many others; combined with our own sustained interest in…”
BackgroundToFU: Topology functional units for deep learning
Oballe, C., Boothe, D. L., Franaszczuk, P. J., & Maroulas, V. (2021) · Foundations of Data Science · doi:10.3934/fods.2021021 · Cited by 6
Geometric and Topological Inference for Deep Representations of Complex Networks
Lin, B. (2022) · International World Wide Web Conference (WWW) · doi:10.1145/3487553.3524194 · Cited by 4
Metric Structures on Datasets: Stability and Classification of Algorithms
Mémoli, F. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23678-5_1 · Cited by 4
“linkage based methods, spectral clustering, k-means, etc); – obtaining a dendrogram: the result of applying a hierarchical clustering algorithm to the data; – computing the average distance to the barycenter of the dataset (assumed to be embedded in Euclidean space); – computing the average distance between all pairs…”
MethodCone Fields and Topological Sampling in Manifolds with Bounded Curvature
Turner, K. (2013) · Foundations of Computational Mathematics · doi:10.1007/s10208-013-9176-6 · Cited by 4
Baby morse theory in data analysis
Marzban, C., & Yurtsever, U. (2011) · doi:10.1145/2023568.2023576 · Cited by 4
“The motivation to organize, associate, and connect multi-dimensional data in order to qualitatively understand its global content has recently led to the development of new tools inspired by topological methods of mathematics [5, 17, 19, 21, 23].”
BackgroundInvariance properties of the Multidimensional matching distance in Persistent Topology and HomologyPreprint
Cerri, A., & Frosini, P. (2010) · arXiv (Cornell University) · doi:10.6092/unibo/amsacta/2765 · Cited by 5
“In order to compare them, the concept of persistent homology group has been defined, and the scientific community has become more and more interested in this subject, both from the theoretical [3, 7, 21, 27] and the applicative point of view [4, 5, 9, 11, 14, 19, 20, 28, 31].”
BackgroundVisualizing Clusters in Artificial Neural Networks Using Morse Theory
Pearson, P. T. (2013) · Advances in Artificial Neural Systems · doi:10.1155/2013/486363 · Cited by 4
“Topological data analysis (TDA) is an emerging field of mathematics that focuses on constructing topologicalmodels for data and calculating algebraic invariants of such models [1–3].”
BackgroundGeometry in the space of persistence modules
de Silva, V., & Nanda, V. (2013) · Annual Symposium on Computational Geometry (SoCG) · doi:10.1145/2462356.2462402 · Cited by 4
“The theory of persistence has been at the vanguard of such analysis [4, 11, 9]: for instance, the persistent homology of a point cloud provides a global multi-scale description of the appearance and disappearance of topological features as the points are thickened into balls”
BackgroundEmergence of core-periphery structure from local node dominance in social networks
Gamble, J., Chintakunta, H., & Krim, H. (2015) · European Signal Processing Conference (EUSIPCO) · doi:10.1109/eusipco.2015.7362716 · Cited by 4
“The word topology here is used in a precise way, as quantified by homology groups in algebraic topology [15], a major tool in the emerging field of topological data analysis [16].”
MethodHyperedge Communities and Modularity Reveal Structure for Documents
Xie, Z. Y., Yi, D., Ouyang, Z.-Z., & Li, D. (2012) · Chinese Physics Letters · doi:10.1088/0256-307x/29/3/038902 · Cited by 4
Topological image texture analysis for quality assessment
Asaad, A., Rashid, R. D., & Jassim, S. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2268471 · Cited by 4
“The features that are remain after changing the threshold are considered to be the features that are conveying true information about the data whereas the features that are not persistent by increasing the threshold gradually considered to be noise [18] [19].”
BackgroundA Probabilistic Topological Approach to Feature Identification Using a Stochastic Robotic Swarm
Ramachandran, R. K., Wilson, S., & Berman, S. (2018) · Springer proceedings in advanced robotics · doi:10.1007/978-3-319-73008-0_1 · Cited by 4
Certified Mapper: Repeated Testing for Acyclicity and Obstructions to the Nerve Lemma
Vejdemo‐Johansson, M., & Leshchenko, A. (2020) · Abel symposia · doi:10.1007/978-3-030-43408-3_19 · Cited by 4
Topological Data Analysis for Identifying Critical Transitions in Cryptocurrency Time Series
Saengduean, P., Noisagool, S., & Chamchod, F. (2020) · doi:10.1109/ieem45057.2020.9309855 · Cited by 4
“Generally, it is based on a combination of two subjects, algebraic and topology, that many researchers are familiar with [8, 9].”
BackgroundMethodTopological constraints and kernel-based density estimation
Pokorny, F. T., Ek, C. H., Kjellström, H., et al. (2012) · Bristol Research (University of Bristol) · Cited by 4
$P$-persistent homology of finite topological spacesPreprint
Vaccarino, F., Patania, A., & Petri, G. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1502.04873 · Cited by 3
“The study of topological spaces and the related computational methods are receiving an unprecedented attention from fields as diverse as biology and social sciences [9], [22], [23], [30] and [31].”
BackgroundAdaptive tracking of representative cycles in regular and zigzag persistent homologyPreprint
Gamble, J., Chintakunta, H., & Krim, H. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1411.5442 · Cited by 4
“The field of topological data analysis [2] has been blossoming in recent years, and many more statisticians, computer scientists and engineers are beginning to use topological tools to study their data.”
BackgroundGromov-Hausdorff Approximation of Metric Spaces with Linear StructurePreprint
Chazal, F., & Sun, J. Z. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1305.1172 · Cited by 3
“We illustrate the performances of our algorithm on a few synthetic and real data sets.”
MethodOn the Vanishing of Homology in Random \v{C}ech ComplexesPreprint
Bobrowski, O., & Weinberger, S. (2015) · arXiv (Cornell University) · Cited by 4
Persistent Homology of Delay EmbeddingsPreprint
Emrani, S., Gentimis, T., & Krim, H. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1305.3879 · Cited by 4
“A S PREVIOUSLY noted in [1], persistent homology can be a powerful topological approach for analyzing large data sets.”
BackgroundLimit Theorems for the Sum of Persistence BarcodesPreprint
Owada, T. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1604.04058 · Cited by 3
“Topological Data Analysis is a growing research area that broadly refers to the analysis of high-dimensional and incomplete datasets, using concepts from algebraic topology, while borrowing ideas and techniques from other fields in mathematics [13].”
BackgroundPersistent Homology of Asymmetric Networks: An Approach based on Dowker FiltrationsPreprint
Chowdhury, S., & Mémoli, F. (2016) · arXiv (Cornell University) · Cited by 7
Novak, T. P. (2016) · SSRN Electronic Journal · doi:10.2139/ssrn.2840962 · Cited by 4
Multiscale persistent functions for biomolecular structure characterizationPreprint
Xia, K., Li, Z., & Mu, L. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.08311 · Cited by 4
“…characterization identification and analysis (CIA) and has been used in a variety of fields, including shape recognition, 17 network structure, 32,38,56 image analysis, 3,10,25,51,57 data analysis, 9,40,47,52,61 chaotic dynamics verification, 34,43 computer vision 57 and computational biology.”
MethodVector-Valued Spectral Analysis of Space-Time DataPreprint
Giannakis, D., Sławińska, J., Ourmazd, A., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.09134 · Cited by 4
“(2) When Q is small, the φk are approximately constant on the level sets of the observation map F (viewed as a scalar function on Ω); this is useful for applications requiring estimation of level sets of functions from noisy data, such as topological data analysis [37].”
BackgroundBaudot, P. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.04520 · Cited by 6
“Concerning neuroscience and cognition, as already mentioned in the cell assembly section, following the development of topological data analysis (which is mainly based on persistence homology [360]), several studies have been dedicated to the application of topological data analysis to visual activity by Singh and col…”
MethodCellular Sheaves And Cosheaves For Distributed Topological Data Analysis
Yoon, I. H. R. (2018) · ScholarlyCommons (University of Pennsylvania) · Cited by 5
Replays of spatial memories suppress topological fluctuations in cognitive mapPreprint
Babichev, A., Morozov, D., & Dabaghian, Y. A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.00941 · Cited by 4
“…Petri et al., 2014) studies, and allows using a powerful Network Neuroscience arsenal of methods from algebraic topology, in particular persistent (Carlsson, 2009; Lum et al., 2013; Singh et al., 2008) and zigzag (Carlsson & De Silva, 2010; Carlsson, De Silva, & Morozov 2009) homology theory…”
Topological Persistence in Geometry and AnalysisPreprint
Polterovich, L., Rosen, D., Samvelyan, K., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.04044 · Cited by 4
“ng of the millennium aimed, in particular, at extracting robust information from noisy topological patterns. We refer to the articles by H. Edelsbrunner and J. Harer [27], R. Ghrist [34], G. Carlsson [15], S. Weinberger [91], U. Bauer and M. Lesnick [7] and the monographs by H. Edelsbrunner [26], S. Oudot [65], F. Cha…”
Elyasi, N., & Moghadam, M. H. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.01726 · Cited by 4
Sliding Windows and Persistence: An Application of Topological Methods to Signal AnalysisPreprint
Perea, J. A., & Harer, J. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1307.6188 · Cited by 4
“gy [12]. Persistent homology is a topological method for measuring the shapes of spaces and the features of functions. One of the most important applications of persistent homology is to point clouds [3], where shape is usually interpreted as the geometry of some implicit underlying object near which the point cloud i…”
MethodA Machine-Learning-Based Early Warning System Boosted by Topological Data AnalysisPreprint
Basu, D., & Li, T. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3394704 · Cited by 4
“For detailed exposition of concepts such as persistent homology and persistence landscape, there are several excellent overviews (see for example Carlsson 2009, Edelsbrunner and Harer 2010 for a general introduction and Perea and Harer 2015 for applications to time series), and we refer to Gidea…”
Unveiling the molecular mechanism of SARS-CoV-2 main protease inhibition from 92 crystal structuresPreprint
Nguyen, D. D., Gao, K., Chen, J., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.13653 · Cited by 4
“hich has the capacity to track the multiscale topological information over different scales along with filtration by characterizing independent components, rings, and higher dimensional voids in space [21]. In this section, we will briefly review the algebraic topology-based representations. Additionally, since we are d…”
BackgroundThe Emergence of Higher-Order Structure in Scientific and Technological Knowledge Networks
Gebhart, T., & Funk, R. J. (2023) · Academy of Management Proceedings · doi:10.5465/amproc.2023.240bp · Cited by 4
From trees to barcodes and back again: theoretical and statistical perspectivesPreprint
Kanari, L., Garin, A., & Hess, K. P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.11620 · Cited by 4
“ough geometric approaches to analyzing data have been extensively used for many years, the rst topological methods for data analysis were developed only recently, e.g., [8], [7], [22], [26], [25] and [4]. Topological Data Analysis (TDA) is a fairly new eld at the intersection of data science and algebraic topology, th…”
MethodA topological perspective on regimes in dynamical systemsPreprint
Strømmen, K., Chantry, M., Dorrington, J., et al. (2021) · arXiv (Cornell University) · Cited by 4
Application of Persistent Homology in Forecasting Realized VolatilityPreprint
Souto, H. G. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4471531 · Cited by 4
tramoTDA: A trajectory monitoring system using Topological Data Analysis
Esteve, M., & Falcó, A. (2024) · SoftwareX · doi:10.1016/j.softx.2024.101953 · Cited by 4
Change Points Detection in Financial Market Using Topological Data AnalysisPreprint
Yao, J. Q., Li, J., Wu, J., Yang, M., & Wang, X. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5196633 · Cited by 4
Optimal Cheeger cuts and bisections of random geometric graphs
Müller, T., & Penrose, M. D. (2020) · The Annals of Applied Probability · doi:10.1214/19-aap1534 · Cited by 3
“A significant recent theme in topological/geometrical data analysis and machine learning is the reconstruction of topological/geometrical properties of a continuous space such as a manifold from a random sample of points in that space via a graph, or more generally a simplicial complex, derived from the sample by conn…”
BackgroundBergomi, M. G., Ferri, M., & Zuffi, L. (2020) · Communications in Applied and Industrial Mathematics · doi:10.2478/caim-2020-0005 · Cited by 3
“On ∆+ = {(u, v) ∈ R2 |u < v} we can then define the k-Persistent Betti Number (k-PBN) function (also dubbed Rank Invariant [7]; an equivalent representation in the references is the so-called persistence barcode) βk(X,f) : ∆ + → Z (u, v) 7→ dim Im(ιr(u,v)) The theory can be extended in a natural way to filtered comple…”
BackgroundTopology-aware loss for segmentation in computed tomography images
Ozcelik, S., Ünver, S., Gürses, İ. A., Türkay, R., & Gündüz-Demir, Ç. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.109512 · Cited by 3
“A variant of homology that is more convenient for realworld applications is the persistent homology [14], [15].”
Urashima, K., Ichinose, K., Ishimaru, H., Kumazaki, H., Kawakami, A., & Ueki, M. (2025) · PLoS ONE · doi:10.1371/journal.pone.0329859 · Cited by 3
Topological elastic metamaterials for wave manipulation: a comprehensive review
Chen, Z., Zhi, G., Wang, G., Guo, T., & Chang, S. (2025) · Journal of Physics Condensed Matter · doi:10.1088/1361-648x/ae170e · Cited by 7
“As a branch of mathematics, topology is devoted to the study of spatial properties that remain invariant under continuous transformations, focusing on shape and structure rather than distance or angle [33–36].”
Rashidi, M., Mousavi, V., Perera, S., & Devitt, J. (2025) · Measurement · doi:10.1016/j.measurement.2025.119713 · Cited by 4
Rojas, L., Jorquera, L., & García, J. (2026) · Mathematics · doi:10.3390/math14050763 · Cited by 3
Carr, E., Carrière, M., Michel, B., et al. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04360-9 · Cited by 3
Stratifying the space of barcodes using Coxeter complexes
Brück, B., & Garin, A. (2022) · Journal of Applied and Computational Topology · doi:10.1007/s41468-022-00104-7 · Cited by 3
“(2020); Curry et al. (2021) to study the space of barcodes from a combinatorial point of view. It extends the approach of considering permutations to classify barcodes to a finer classification that also takes into account the average and standard deviation of births and deaths. In Xu (2020), the author also observes…”
BackgroundMethodInfluentialDVT-Net: A Multimodal Deep Vascular Topology Network for Disease Prediction
Tian, Y., Wu, G. K., Bearelly, S., et al. (2023) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi53787.2023.10230374 · Cited by 3
“try to infer the structure of data [16, 17].”
BackgroundInformation exploitation of human resource data with persistent homology
Chong, W. K., Chang, C.-C., & Rudkin, S. (2023) · Journal of Business Research · doi:10.1016/j.jbusres.2023.114410 · Cited by 3
Comparing Mapper Graphs of Artificial Neuron Activations
Zhou, Y., Jenne, H., Brown, D., Shapiro, M. R., Jefferson, B. A., Joslyn, C., Henselman‐Petrusek, G., Praggastis, B., Purvine, E., & Wang, B. (2023) · Topological Data Analysis and Visualization (TopoInVis) · doi:10.1109/topoinvis60193.2023.00011 · Cited by 4
“E-mails: zhouyj96180@gmail.com, beiwang@sci.utah.com • H. Jenne, D. Brown A key strength of the mapper graph is that it is coordinate and deformation invariant [16], making it suitable for examining data across participants or subgroups [64].”
BackgroundIdentification of Autism Spectrum Disorder Using Topological Data Analysis
Zhang, X., Gao, Y., Zhang, Y., Li, F., Li, H., & Lei, F. (2024) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-024-01002-3 · Cited by 3
“For more details about the TDA, persistent homology and its representation, we can refer to [24, 25, 32].”
An Investigation of Time Series Embeddings and Topological Data Analysis for Fault Analysis
Lee, D., Rorie, J., & Sabater, A. B. (2023) · doi:10.1109/csce60160.2023.00245 · Cited by 2
“Persistent homology [13] is a method for characterizing the shape of data.”
MethodTopological Fingerprints for Audio Identification
Reise, W., Fernández, X., Dominguez, M., Harrington, H. A., & Beguerisse-Díaz, M. (2024) · SIAM Journal on Mathematics of Data Science · doi:10.1137/23m1605090 · Cited by 8
Procedure to reveal the mechanism of pattern formation process by topological data analysis
Mototake, Y.-I., Mizumaki, M., Kudo, K., et al. (2024) · Physica D Nonlinear Phenomena · doi:10.1016/j.physd.2024.134359 · Cited by 3
Conley Index for Multivalued Maps on Finite Topological Spaces
Barmak, J. A., Mrożek, M., & Wanner, T. (2024) · Foundations of Computational Mathematics · doi:10.1007/s10208-024-09685-4 · Cited by 3
“The discretization in time is achieved by means of numerical methods and leads to discrete-time dynamical systems, a subject interesting in itself, regardless of potential computer applications.”
Induction Motor Fault Classification with Topological Data Analysis
Wang, B. (2024) · IEEE Energy Conversion Congress and Exposition (ECCE) · doi:10.1109/ecce55643.2024.10860892 · Cited by 3
“TDA extracts the shape information for a given data space, such as connected components, holes, and other higher dimensional features [6].”
Zhu, J., Gao, S., Tao, C., Liu, Y., & Li, H. (2025) · Transactions in GIS · doi:10.1111/tgis.70081 · Cited by 3
“Mathematically, topology involves the study of topological invariants preserved under continuous deformation, which can be used to characterize data (Carlsson 2009).”
InfluentialRoughness-informed machine learning – A call for fractal and fractional calculi
Partohaghighi, M., Marcia, R. F., West, B. J., & Chen, Y. (2025) · Journal of Information and Intelligence · doi:10.1016/j.jiixd.2025.09.001 · Cited by 3
“…weight distributions amplify sensitivity to input perturbations, oscillatory loss landscapes hinder convergence, complex parameter manifolds complicate optimization, and topological irregularities increase model complexity (Zhang et al., 2017; Li et al., 2018; Martens, 2014; Carlsson, 2009).”
Topological Sequence Analysis of Genomes: Category Theory Approaches
Liu, J., Shen, L., Zia, M., et al. (2025) · Journal of Computational Biophysics and Chemistry · doi:10.1142/s2737416526400016 · Cited by 3
“Topological Data Analysis (TDA) is a class of techniques rooted in algebraic topology, designed to extract and analyze the intrinsic topological features of complex data [2, 4, 34].”
Some Geometrical and Topological Aspects of Dimensionality Reduction in Signal Analysis
Guillemard, M. (2012) · Cited by 3
RETRACTED CHAPTER: Hyper Klein Bottle Logophysics Ontopoiesis of the Cosmos and Life
Rapoport, D. L. (2013) · Springer eBooks · doi:10.1007/978-3-319-02039-6_22 · Cited by 3
RetractedDynamics of Evolving Feed-Forward Neural Networks and Their Topological Invariants
Masulli, P., & Villa, A. E. P. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-44778-0_12 · Cited by 3
“These constructions have been applied successfully to the field of data science [4], proving to be a powerful tool to understand the inner structure of a data set by representing it as a sequence of topological spaces, and more recently to neuroscience [6,8,13,14].”
BackgroundPersistent homology in graph power filtrations
Parks, A., & Marchette, D. J. (2016) · Royal Society Open Science · doi:10.1098/rsos.160228 · Cited by 3
“Topological data analysis is concerned with determining the topological structure of data [1].”
BackgroundLearning and modelling big data
Hammer, B., He, H., & Martinetz, T. (2014) · Cited by 3
Dynamics of silo deformation under granular discharge
Colonnello, C., & Kramár, M. (2018) · Physical review. E · doi:10.1103/physreve.98.052902 · Cited by 3
Geometric and probabilistic limit theorems in topological data analysis
Kališnik, S., Lehn, C., & Limic, V. (2021) · Advances in Applied Mathematics · doi:10.1016/j.aam.2021.102244 · Cited by 3
“The philosophy of topological data analysis is that data does have an inherent topology and in order to uncover it, one assigns a 1-parameter family of topological spaces or a filtration to a finite metric space M [Car09, Car14, ELZ02].”
InfluentialFock Space Representation of the Circle Quantum Group
Sala, F., & Schiffmann, O. (2019) · International Mathematics Research Notices · doi:10.1093/imrn/rnz268 · Cited by 3
A Primer on Persistent Homology of Finite Metric Spaces
Mémoli, F., & Singhal, K. (2019) · Bulletin of Mathematical Biology · doi:10.1007/s11538-019-00614-z · Cited by 6
Merge trees in discrete Morse theory
Johnson, B., & Scoville, N. A. (2022) · Research in the Mathematical Sciences · doi:10.1007/s40687-022-00347-x · Cited by 3
“Persistence has been used to study statistical mechanics [20], hypothesis testing [6], image analysis [7], complex networks [19], and many other phenomena.”
Method
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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