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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
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.
- 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
“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.
- 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
“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.
- 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
“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
Cites
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
Bootstrap methods: another look at the jackknife
Efron, B. (1979) · The Annals of Statistics · doi:10.1214/aos/1176344552 · Cited by 17,602
Density Estimation for Statistics and Data Analysis
Silverman, B. W. (2018) · Routledge eBooks · doi:10.1201/9781315140919 · Cited by 15,938
Nonlinear Dimensionality Reduction by Locally Linear Embedding
Roweis, S. T., & Saul, L. K. (2000) · Science · doi:10.1126/science.290.5500.2323 · Cited by 14,965
A Global Geometric Framework for Nonlinear Dimensionality Reduction
Tenenbaum, J. B., Silva, V. D., & Langford, J. C. (2000) · Science · doi:10.1126/science.290.5500.2319 · Cited by 13,585
Categories for the Working Mathematician
Lane, S. M. (1971) · Graduate texts in mathematics · doi:10.1007/978-1-4612-9839-7 · Cited by 8,982 · cited 3×
“We note that the correspondence X → π0(X) can actually be viewed as a functor (see [44]) from the category of topological spaces to the category of sets, in the sense that a continuous map f : X → Y induces a map of sets π0(f) : π0(X)→ π0(Y ), satisfying certain obvious conditions on composite maps and identity maps.”
BackgroundMethodDensity Estimation for Statistics and Data Analysis
(1986) · Springer eBooks · doi:10.1007/978-1-4899-3324-9 · Cited by 8,569
(1975) · CERN Document Server (European Organization for Nuclear Research) · Cited by 5,958
Density Estimation for Statistics and Data Analysis
Dehnad, K., & Silverman, B. W. (1987) · Technometrics · doi:10.2307/1269475 · Cited by 5,479
(2001) · Cited by 5,374
Research works printed but not linked: 4
- tigran G. Carlsson and T. Ishkanov, Local structure of spaces of natural images, preprint, (2007), available at http://comptop.stanford.edu/preprints/
- Dummit, David S., 2004, Abstract algebra
- Huber, Peter J., 1985, Projection pursuit, Ann. Statist., 13, 435, 10.1214/aos/1176349519 · DOI · Paper page
- Reeb, Georges, 1946, Sur les points singuliers d’une forme de Pfaff complètement intégrable ou d’une fonction numérique, C. R. Acad. Sci. Paris, 222, 847
Cited by
Sorted by citations
Items 1,001–1,100 of 2,331
Necessary conditions for discontinuities of multidimensional persistent Betti numbers
Cerri, A., & Frosini, P. (2014) · Mathematical Methods in the Applied Sciences · doi:10.1002/mma.3093 · Cited by 7
“Here the term multidimensional, or equivalently k-dimensional, refers to the fact that data properties are described through functions taking values in R [9,28,35].”
BackgroundSchwabe, O., Shehab, E., & Erkoyuncu, J. A. (2016) · Progress in Aerospace Sciences · doi:10.1016/j.paerosci.2016.05.001 · Cited by 7
Generalized penalty for circular coordinate representation
Luo, H., Patania, A., Kim, J., & Vejdemo‐Johansson, M. (2021) · Foundations of Data Science · doi:10.3934/fods.2021024 · Cited by 7
“Like standard Topological Data Analysis (TDA) techniques, we approximate the underlying space M by constructing an approximating complex Σ, like Vietoris-Rips complex or Čech complex [10].”
MethodTopology Preserving Data Reduction for Computing Persistent Homology
Malott, N. O., Sens, A. M., & Wilsey, P. A. (2020) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata50022.2020.9378216 · Cited by 8
“Although some partitioning methods may result in more complete solutions, in general most d -dimensional spatial partitioning approaches will work [39].”
BackgroundMethodInfluentialStatistical Learning via Manifold Learning
Bernstein, A., Kuleshov, A., & Yanovich, Y. (2015) · IEEE International Conference on Machine Learning and Applications (ICMLA) · doi:10.1109/icmla.2015.26 · Cited by 7
“Various geometric methods, which are used for finding of the geometrical and topological structure in data, have now become the central methodology for statistical learning tasks [11-14].”
MethodCarlsson, G. (2012) · Cambridge University Press eBooks · doi:10.1017/cbo9781139095402.003 · Cited by 7
Self-citationThe Landscape of Complex NetworksPreprint
Weinan, E., Lu, J., & Yao, Y. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1204.6376 · Cited by 7
“But one can endow a network with more sophisticated structures, such as geometric structure as in the theory of manifold learning, [5, 6, 7, 8, 9, 10], or topological structure as in the theory of persistent homology [11, 12, 13, 14].”
BackgroundMethodComplex Politics: A Quantitative Semantic and Topological Analysis of UK House of Commons DebatesPreprint
Gurciullo, S., Smallegan, M. J., Pereda, M., Battiston, F., Patania, A., Poledna, S., Hedblom, D., Öztan, B. T., Herzog, A., John, P. C., et al. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1510.03797 · Cited by 7
“Topological Data Analysis (TDA) is a relatively new area of research first introduced in 2002 by Gunnar Carlsson [29].”
BackgroundTopology Analysis of International Networks Based on Debates in the United NationsPreprint
Gurciullo, S., & Mikhaylov, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.09491 · Cited by 7
Persistent cohomology for data with multicomponent heterogeneous informationPreprint
Cang, Z., & Wei, G. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.11120 · Cited by 7
Sampling random graph homomorphisms and applications to network data analysisPreprint
Lyu, H., Mémoli, F., & Sivakoff, D. J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.09483 · Cited by 7
“However, as is well known in the TDA community, computing persistence diagrams for large data sets is computationally expensive (see [25, 65] for earlier algorithms and [7, 24, 51, 45] for recent surveys).”
BackgroundUnsupervised Space-Time Clustering using Persistent HomologyPreprint
Islambekov, U., & Gel, Y. R. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.11525 · Cited by 7
“For more details on TDA, the reader may consult survey papers such as [21], [7], [22], [23].”
BackgroundPrediction and mitigation of mutation threats to COVID-19 vaccines and antibody therapiesPreprint
Chen, J., Gao, K., Wang, R., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.06357 · Cited by 7
“learning and algebraic topology [44–46].”
PLLay: Efficient Topological Layer based on Persistence LandscapesPreprint
Kim, K., Kim, J., Zaheer, M., Kim, J. S., Chazal, F., & Wasserman, L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.02778 · Cited by 11
“Topological data analysis (TDA) is a recent and emerging field of data science that relies on topological and geometric tools to infer relevant features for possibly complex data Carlsson (2009).”
BackgroundPérez, J. B., Hauke, S., Lupo, U., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.05412 · Cited by 7
“…has been a key driving force behind the ever-increasing adoption of topological approaches in a wide variety of computational contexts, such as geometric inference [18, 6], signal processing [40, 36], data visualization [43], and, more generally, data analysis [8, 12] and machine learning [26].”
BackgroundContinuous Indexing of Fibrosis (CIF): Improving the Assessment and Classification of MPN PatientsPreprint
Ryou, H., Sirinukunwattana, K., Aberdeen, A., et al. (2022) · medRxiv · doi:10.1101/2022.06.06.22276014 · Cited by 7
Discrete Morse theory for filtrations
Nanda, V. (2012) · Rutgers University Community Repository (Rutgers University) · doi:10.7282/t3q52n6n · Cited by 8
“4 surveys the relatively new field of persistent homology [15, 7, 40, 10] which is defined for filtered cell complexes.”
BackgroundAssuring Safety-Critical Machine Learning Enabled Systems: Challenges and Promise
Goodloe, A. E. (2022) · 2022 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) · doi:10.1109/issrew55968.2022.00088 · Cited by 14
“Mathematicians working in the area of topological data analysis [42]–[44] are bringing to bear powerful techniques from algebraic and differential topology as well as differential and algebraic geometry that allow us to compute geometric and topological invariants on the data.”
BackgroundData-Efficient Machine Learning Methods for Electric Motor Surrogate Models
Wang, B., & Sakamoto, Y. (2024) · IEEE Biennial Conference on Electromagnetic Field Computation (CEFC) · doi:10.1109/cefc61729.2024.10585794 · Cited by 6
“Compared with other straightforward geometric methods, which quantitatively describe geometric properties such as curvatures, topology describes geometric properties in a much less sensitive way to the choice of metrics [7].”
MethodQuotient Complex (QC)-Based Machine Learning for 2D Hybrid Perovskite Design
Hu, C.-S., Mayengbam, R., Xia, K., & Sum, T. C. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c02033 · Cited by 8
Enhancing energy predictions in multi-atom systems with multiscale topological learning
董, C. 晨. D., Wang, R., Wei, G.-W., & Pan, F. (2025) · Journal of Materials Chemistry A · doi:10.1039/d5ta02687c · Cited by 6
“20 – 25 And the Quotien Complex was recently introduced to study the inorangic system.”
The topology of molecular representations and its influence on machine learning performance
Rottach, F., Schieferdecker, S., & Eickhoff, C. (2025) · Journal of Cheminformatics · doi:10.1186/s13321-025-01045-w · Cited by 10
“For a detailed overview, we suggest [51], while more in-depth background can be found in the works of Hatcher [52] and Carlsson [53].”
Application of topological data analysis to flood disaster management in Nigeria
Ohanuba, F. O., Ismail, M. T., & Ali, M. K. M. (2022) · Environmental Engineering Research · doi:10.4491/eer.2022.411 · Cited by 6
Topological Regularization for Representation Learning via Persistent Homology
Chen, M., Wang, D., Shi, F., & Zhang, Y. (2023) · Mathematics · doi:10.3390/math11041008 · Cited by 7
“The advantages of TDA methods [8,11–13] are as follows: (1) TDA studies the global “shape” of data and explores the underlying topological and geometric structures of point clouds.”
BackgroundMethodInfluentialTight basis cycle representatives for persistent homology of large biological data sets
Aggarwal, M., & Periwal, V. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010341 · Cited by 6
“Why is the location information elided? By definition, representative boundaries around topological features are not uniquely defined [Carlsson, 2009] so the resulting boundaries can be geometrically imprecise (Figure 1d).”
Ensemble Learning for Mapper Parameter Optimization
Fitzpatrick, P., Jurek, A., Dłotko, P., et al. (2023) · Proceedings - International Conference on Tools with Artificial Intelligence, TAI · doi:10.1109/ictai59109.2023.00026 · Cited by 6
“The Mapper algorithm, introduced in [5], is a Topological Data Analysis (TDA) technique used to visualize data as a graph with nodes representing sample clusters and edges de-noting similarity.”
MethodThe topology of data hides in quantum thermal states
Scali, S., Umeano, C., & Kyriienko, O. (2024) · APL Quantum · doi:10.1063/5.0209201 · Cited by 15
The persistent topology of optimal transport based metric thickenings
Adams, H., Mémoli, F., Moy, M., et al. (2024) · Algebraic & Geometric Topology · doi:10.2140/agt.2024.24.393 · Cited by 6
Restrepo, M., & Cornelis, C. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-65665-1_1 · Cited by 6
Class Incremental Learning via Semantic Information Mapping and Background Information Calibrating
Xian, Y., Yu, H. Z., Li, H., & Wang, G. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3447066 · Cited by 7
Persistent homology approach for uncovering transitions to Chaos
Shah, W. H., Jaimes-Reátegui, R., Huerta-Cuellar, G., García-López, J. H., & Pisarchik, A. N. (2025) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2025.116054 · Cited by 7
Reservoir computing approaches to unsupervised concept drift detection in dynamical systems
Thorne, B., Corrêa, D., Zaitouny, A., et al. (2025) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0234779 · Cited by 6
A Big Data application framework for consumer behavior analysis
Zin, T. T., Tin, P., Toriu, T., et al. (2013) · IEEE Global Conference on Consumer Electronics (GCCE) · doi:10.1109/gcce.2013.6664813 · Cited by 6
Invariants for tame parametrised chain complexes
Chachólski, W., Giunti, B., & Landi, C. (2021) · Homology Homotopy and Applications · doi:10.4310/hha.2021.v23.n2.a11 · Cited by 6
“The point of view of homotopy theory is entering the TDA subject also for purposes different from ours.”
BackgroundInfluentialGreulich, P., Smith, R., & MacArthur, B. D. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-817996-3.00003-7 · Cited by 7
A language to analyze, describe, and explore collections of visual art
Pflüger, H. (2021) · Visual Computing for Industry Biomedicine and Art · doi:10.1186/s42492-021-00071-3 · Cited by 6
Topological Data Analysis Of Eye Movements
Kachan, O., & Onuchin, A. A. (2021) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi48211.2021.9433898 · Cited by 6
“TDA [9] introduces a new class of continuous vector-valued feature maps for a wide range of data domains such as point clouds, time series, and scalar fields summarizing the topology of the data in a multiscale way (Fig.”
BackgroundIn Silico Generation of a Topologically Diverse Zeolite-Templated Carbon Library
Wu, J., Zhang, K., Yoo, H., et al. (2021) · Crystal Growth & Design · doi:10.1021/acs.cgd.1c00620 · Cited by 6
Beshkov, K., & Tiesinga, P. H. E. (2021) · Biological Cybernetics · doi:10.1007/s00422-021-00906-5 · Cited by 6
“structure and neural representation, is through the application of both linear and non-linear dimensionality reduction methods (Gao and Ganguli (2015), Gallego et al.”
MethodInfluentialTopological analysis of the latent geometry of a complex network
Jhun, B. (2022) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0073107 · Cited by 7
Topological data analysis of black hole images
Christian, P., Chan, C., Hsu, A., et al. (2022) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.106.023017 · Cited by 6
Manifold learning for coherent design interpolation based on geometrical and topological descriptors
Muñoz, D., Allix, O., Chinesta, F., et al. (2023) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2022.115859 · Cited by 6
“We propose to employ the Topological Data Analysis (TDA) [6,7] that encompasses a set of tools from high-dimensional data analysis which extract topological information from a group of points.”
MethodPartial matchings induced by morphisms between persistence modules
González-Dı́az, R., Soriano-Trigueros, M., & Torras-Casas, Á. (2023) · Computational Geometry · doi:10.1016/j.comgeo.2023.101985 · Cited by 6
“However, f produces the following: [1, 2]1 f ⟼ [1, 2]1 , [2, 2]1 f ⟼ ∅ which is counter-intuitive.”
BackgroundMethodInfluentialFormation of features based on computational topology methods
Chukanov, S. N. (2023) · Computer Optics · doi:10.18287/2412-6179-co-1190 · Cited by 6
Singh, M. K., Chaube, S., Pant, S., Singh, S. K., & Kumar, A. (2023) · Decision Analytics Journal · doi:10.1016/j.dajour.2023.100253 · Cited by 6
A topological based feature extraction method for the stock market
Chang, C., & Lin, H. (2023) · Data Science in Finance and Economics · doi:10.3934/dsfe.2023013 · Cited by 6
Assuring Safety-Critical Machine Learning-Enabled Systems: Challenges and Promise
Goodloe, A. E. (2023) · Computer · doi:10.1109/mc.2023.3266860 · Cited by 7
Large deviation principle for geometric and topological functionals and associated point processes
Hirsch, C., & Owada, T. (2023) · The Annals of Applied Probability · doi:10.1214/22-aap1914 · Cited by 6
Srinivasan, S., Kaluskar, K., Broderick, S., & Rajan, K. (2015) · Ultramicroscopy · doi:10.1016/j.ultramic.2015.04.009 · Cited by 6
“101 information in different dimensions of space [16].”
BackgroundOn certain optimal diffeomorphisms between closed curves
Cerri, A., & Fabio, B. D. (2011) · Forum Mathematicum · doi:10.1515/form.2011.172 · Cited by 6
Statistical analysis of protein ensembles
Máté, G., & Heermann, D. W. (2014) · Frontiers in Physics · doi:10.3389/fphy.2014.00020 · Cited by 6
“Our method relies on the calculation of the persistence intervals of the Betti numbers [12] for the investigated structures.”
MethodJussi Karlgren, C., Bohman, M., Ekgren, A., Isheden, G., Kullmann, E., & Nilsson, D. (2014) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2661829.2662028 · Cited by 6
“[1] A topological perspective of the data affords us an effective view of the structure of models, and is useful for the diagnosis and practical quality assessment of models which already have proven to be of value in real-world applications.”
BackgroundAaron, C., & Cholaquidis, A. (2020) · Annales de l Institut Henri Poincaré Probabilités et Statistiques · doi:10.1214/19-aihp1027 · Cited by 6
“We refer to Chevalier (1976) and Devroye and Wise (1980) for precursor works on support estimation, Cuevas and Fraiman (2010) for a review on support estimation, Cuevas and Rodriguez-Casal (2004) for boundary estimation, Cuevas et al. (2007) for boundary measure estimation, Berrendero et al.”
MethodTopology, Big Data and Optimization
Vejdemo‐Johansson, M., & Škraba, P. (2016) · Studies in big data · doi:10.1007/978-3-319-30265-2_7 · Cited by 6
Beyond the anatomy-based representation of brain function
Papo, D. (2017) · Physics of Life Reviews · doi:10.1016/j.plrev.2017.04.005 · Cited by 5
Open Problems in Computational Topology
Gasarch, W. I., Fasy, B. T., & Wang, B. (2017) · ACM SIGACT News · doi:10.1145/3138860.3138867 · Cited by 6
“For the interested reader, we suggest [6, 18, 21] for a gentle introduction to computational topology, [15] for algorithmic foundations, and [19] for a (free online) textbook with many examples.”
Martino, A., Santis, E. D., Giuliani, A., & Rizzi, A. (2020) · Entropy · doi:10.3390/e22070794 · Cited by 6
“Topological Data Analysis [99,100] is a novel data analysis approach useful whenever data can be described by topological structures (networks) as it consists in a set of techniques in order to extract information from data (starting from topological information) by means of dimensionality reduction, manifold estimati…”
BackgroundMethodBayesian Topological Learning for Classifying the Structure of Biological Networks
Maroulas, V., Micucci, C. P., & Nasrin, F. (2021) · Bayesian Analysis · doi:10.1214/21-ba1270 · Cited by 9
“…2017, Bendich et al., 2016, Biscio and Møller, 2019, Nasrin et al., 2019], sensor networks [D(cid:32)lotko et al., 2012, Carlsson and de Silva, 2010,Carlsson, 2009,Silva and Ghrist, 2006,Silva and Ghrist, 2007], biology [Sgouralis et al., 2017, Maroulas and Nebenf¨uhr, 2015, Mike et al., 2016,…”
BackgroundVariability in higher order structure of noise added to weighted networks
Blevins, A. S., Kim, J. Z., & Bassett, D. S. (2021) · Communications Physics · doi:10.1038/s42005-021-00725-x · Cited by 6
“Persistent homology [10, 66, 43] records the longevity of topological cavities that form and collapse”
BackgroundBayesian topological signal processing
Oballe, C., Cherne, A., Boothe, D., Kerick, S. E., Franaszczuk, P. J., & Maroulas, V. (2021) · Discrete and Continuous Dynamical Systems - S · doi:10.3934/dcdss.2021084 · Cited by 6
“Persistent homology is the workhorse of topological data analysis (TDA), a set of ideas and tools to make inferences about topological and geometric aspects of data [8].”
BackgroundTopological Data Analysis Highlights Novel Geographical Signatures of the Human Gut Microbiome
Lymberopoulos, E., Gentili, G. I., Alomari, M., & Sharma, N. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.680564 · Cited by 8
“TDA is an analysis method coined by Gunnar Carlsson (2009) and was developed to analyse highdimensional datasets.”
MethodTopological Analysis of Contradictions in Text
Wu, X., Niu, X., & Rahman, R. (2022) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3477495.3531881 · Cited by 9
“This technique in TDA is called Persistent Homology (PH) [2, 9].”
MethodClassification of stochastic processes with topological data analysis
Güzel, İ., & Kaygun, A. (2023) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.7732 · Cited by 6
“We refer interested readers to [6] for an introduction to persistent homology, and more mathematical details on building topological spaces from point clouds.”
BackgroundAsymptotics of lower dimensional zero-density regions
Luo, H., MacEachern, S. N., & Peruggia, M. (2023) · Statistics · doi:10.1080/02331888.2023.2262665 · Cited by 6
“We refer readers who are interested in a detailed treatment of these data analysis techniques to the introductory texts by Carlsson [5] and Edelsbrunner and Harer [9].”
MethodSchwabe, O., Shehab, E., & Erkoyuncu, J. A. (2016) · Procedia CIRP · doi:10.1016/j.procir.2016.08.017 · Cited by 6
“cost variance due to quantity, schedule and engineering, changes) then topological data analysis can be considered applicable since the minimum number of dimensions for such has been arrived at (therefore three) otherwise the cost estimator must depend on using analogies and expert opinion for proceeding with the cost…”
BackgroundMethodCapodieci, L. (2017) · China Semiconductor Technology International Conference (CSTIC) · doi:10.1109/cstic.2017.7919774 · Cited by 6
“) onto a high-dimensional topological network map, using well-established (albeit somewhat esoteric) topological data analysis (TDA) techniques [5].”
A Novel Method for Topological Embedding of Time-Series Data
Kennedy, S. M., Roth, J. D., & Scrofani, J. W. (2018) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco.2018.8553502 · Cited by 6
“When computed in this way, each topological feature is said to be “born” at some smaller resolution and “die” at a larger resolution [1].”
BackgroundMethodInfluentialVandaele, R., Bie, T. D., & Saeys, Y. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-10928-8_2 · Cited by 6
Nori Diagrams and Persistent Homology
Manin, Y. I., & Marcolli, M. (2019) · Mathematics in Computer Science · doi:10.1007/s11786-019-00422-7 · Cited by 6
Assessing the Resilience of the Texas Power Grid Network
Ofori-Boateng, D., Dey, A. K., Gel, Y. R., Li, B., Zhang, J., & Poor, H. V. (2019) · IEEE Data Science Workshop (DSW) · doi:10.1109/dsw.2019.8755787 · Cited by 8
“Alternatively, to study the geometry and topology of power flow networks, we can employ the tools of topological data analysis (TDA) [13, 14, 15, 16, 17].”
BackgroundMethodInfluentialFeng, T., Davila, J. I., Liu, Y., Lin, S., Huang, S., & Wang, C. (2019) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2019.2950657 · Cited by 6
“TDA models do not aim to reconstruct data; instead, they aim to qualitatively infer and summarize the topological structures [16].”
BackgroundPersistent Homology on Streaming Data
Moitra, A., Malott, N. O., & Wilsey, P. A. (2020) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw51313.2020.00090 · Cited by 6
“Persistent homology is a powerful tool for data analysis that extracts information about topological features such as connected components, loops, and voids in a point cloud at different spatial connectivities [1]–[5].”
MethodHadwiger Integration of Definable Functions
Wright, M. (2011) · Scholarly Commons (University of Pennsylvania) · Cited by 6
Synthesis of variable dancing styles based on a compact spatiotemporal representation of dance
Oliveira, J. L., Naveda, L., Gouyon, F., Leman, M., & Reis, L. P. (2010) · Ghent University Academic Bibliography (Ghent University) · Cited by 6
Contributions to Persistence Theory
Du, D. (2014) · Annals of West University of Timisoara - Mathematics and Computer Science · doi:10.2478/awutm-2014-0012 · Cited by 6
“B 0 (f (3); 1) = { [1,∞) } B 0 (f (3); 1) = { (−∞, 1] } B 0 (f (3); 2) = { [2,∞), [2, 5) } B 0 (f (3); 2) = { (−∞, 2], (1, 2] } B 0 (f (3); 3) = { [3,∞), [3, 5), [3, 5) } B 0 (f (3); 3) = { (−∞, 3], (1, 3], (2, 3] } B 0 (f (3); 4) = { [4,∞), [4, 5), [4, 5) } B 0 (f (3); 4) = { (−∞, 4], (1, 4], (2, 4] } B 0 (f (3); 5)…”
BackgroundInfluentialAn Isometry Theorem for Generalized Persistence ModulesPreprint
Meehan, K., & Meyer, D. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1710.02858 · Cited by 6
“cycles which disappear quickly after their appearance (narrow ones) are interpreted as noise and disregarded. This technique has been widely successful in topological data analysis (see, for example, [Car09], [CSEH07], [CdO12], [SG07], [CIdSZ08], [CCR13], [HNH+16], and [GPCI15]). Typically, the category of persistence…”
MethodFibres of Failure: Classifying errors in predictive processesPreprint
Carlsson, L. S., Carlsson, G., & Vejdemo‐Johansson, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.00384 · Cited by 6
Self-citationVietoris-Rips Complexes of Regular PolygonsPreprint
Adams, H., Chowdhury, S., Jaffe, A. Q., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.10971 · Cited by 6
“If X is a sample from some unknown underling metric space M , then the persistent homological features in VR(X; r) are often taken as an estimate of the homology of M , whereas the remaining short-lived features are often regarded as noise [10, 16].”
A look at the topology of convolutional neural networks.Preprint
Gabrielsson, R. B., & Carlsson, G. (2018) · arXiv (Cornell University) · Cited by 6
Self-citationSauerwald, N., Shen, Y., & Kingsford, C. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/540716 · Cited by 7
“In order to derive stability results for TDA, Carlsson (2014) proposed a metric called the bottleneck distance that quantifies the di↵erence between two persistence diagrams.”
BackgroundA Stable Multi-Scale Kernel for Topological Machine LearningPreprint
Reininghaus, J., Huber, S., Bauer, U., & Kwitt, R. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1412.6821 · Cited by 7
“Since then, several authors have demonstrated that TDA can capture characteristics of the data that other methods often fail to provide, c.f .”
BackgroundInfluentialThe stability of persistent homology of hypergraphsPreprint
Ren, S., & Wu, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.02237 · Cited by 6
Capturing Dynamics of Time-Varying Data via TopologyPreprint
Xian, L., Adams, H., Topaz, C. M., & Ziegelmeier, L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.05780 · Cited by 6
“[3;11], [5;10], [5;9], and let 4 = i<j= 8 (see Figure1). Then rank(V(4) !V(8)) is two since there are two intervals ([2;9] and [3;11]) in the persistence diagram that contain the interval [i;j] = [4;8]. The collection of all natural numbers rank(V(i) !V(j)) for all choices of ijis called the rank invariant. 2.4. Th…”
BackgroundInfluentialChain level Steenrod operationsPreprint
Kaufmann, R. M., & Medina-Mardones, A. M. (2020) · arXiv (Cornell University) · Cited by 5
Enhanced perfusion following exposure to radiotherapy: A theoretical investigation
Köry, J., Narain, V., Stolz, B. J., et al. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011252 · Cited by 5
Topological classification of tumour-immune interactions and dynamics
Yang, J., Fang, H., Dhesi, J., Yoon, I. H. R., Bull, J. A., Byrne, H. M., Harrington, H. A., & Grindstaff, G. (2025) · Journal of Mathematical Biology · doi:10.1007/s00285-025-02253-6 · Cited by 11
“At its core, TDA captures multi-scale structural characteristics of data - clusters, loops, and other higher-dimensional features - pro - viding unique insights beyond traditional methods (Carlsson 2009).”
Sparse portfolio selection via topological data analysis based clustering
Goel, A., Filipović, D., & Pasricha, P. (2025) · Quantitative Finance · doi:10.1080/14697688.2025.2544762 · Cited by 6
“The first step involves building a simplicial complex given the point cloud X = { x i } Ni =0 in R q .”
A topology-aware multiscale feature fusion network for EEG-based motor imagery decoding
Shen, C., & Namiki, A. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.114540 · Cited by 5
Risk reduced sparse index tracking portfolio: A topological data analysis approach
Goel, A., Pasricha, P., & Kanniainen, J. (2025) · Omega · doi:10.1016/j.omega.2025.103432 · Cited by 5
Vision toolkit part 2. features and metrics for assessing oculomotor signal: a review
Laborde, Q., Roques, A., Armougum, A., Vayatis, N., Bargiotas, I., & Oudre, L. (2025) · Frontiers in Physiology · doi:10.3389/fphys.2025.1661026 · Cited by 6
“By recording the birth and death of each feature, the structural information of the dataset can be summarized in a persistence diagram, where longer-lived features typically represent meaningful structures while short-lived features correspond to noise (Carlsson, 2009; Edelsbrunner and Harer, 2022).”
Zabaleta-Ortega, Á., Masoller, C., & Guzmán-Vargas, L. (2023) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0167523 · Cited by 5
Catanzaro, M. J., Rizzo, S., Kopchick, J. J., Chowdury, A., Rosenberg, D. R., Bubenik, P., & Diwadkar, V. A. (2023) · Neuroinformatics · doi:10.1007/s12021-023-09645-3 · Cited by 6
“Persistent homology expands upon the notion of homology by tracking the changes to shape over a filtration of the signal, thus providing a multi-scale measure of signal shape (Carlsson, 2009; Edelsbrunner & Harer, 2008; Robinson, 2014; Wang et al., 2018).”
Platt, D. E., Bose, A., Rhrissorrakrai, K., et al. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae235 · Cited by 5
“We seek to recognize this situation by applying computational homology (Carlsson 2009, Edelsbrunner and Harer 2022) to identify topological loops among the pathways.”
Persistent directed flag Laplacian
Jones, B. R., & Wei, G.-W. (2024) · Foundations of Data Science · doi:10.3934/fods.2024048 · Cited by 8
“Persis-tence modules, including persistent (co)homology, are some of the most studied objects in TDA, and can convey information about a system in different dimensions, scales of size, and/or scales of time [37, 5].”
BackgroundIdentifying key genes in cancer networks using persistent homology
Ramos, R. H., Bardelotte, Y. A., Ferreira, C. D. O. L., & Simao, A. (2024) · Scientific Reports · doi:10.1038/s41598-025-87265-4 · Cited by 8
“Topological data analysis (TDA) [18–20] is based on the principle that topology and geometry can be utilised to derive both qualitative and quantitative insights about the underlying structure of data.”
On limit theorems for persistent Betti numbers from dependent data
Krebs, J. (2021) · Stochastic Processes and their Applications · doi:10.1016/j.spa.2021.04.013 · Cited by 5
Comments on: Distance geometry and data science
Lavor, C. (2020) · Top · doi:10.1007/s11750-020-00561-2 · Cited by 5
Persistent Homology Metrics Reveal Quantum Fluctuations and Reactive Atoms in Path Integral Dynamics
Hu, Y., Ounkham, P., Maršálek, O., Markland, T. E., Krishmoorthy, B., & Clark, A. E. (2021) · Frontiers in Chemistry · doi:10.3389/fchem.2021.624937 · Cited by 5
“Within the last decade, concepts from the mathematical field of algebraic topology have been combined with computational methods to characterize the global shape of data (Carlsson, 2009).”
BackgroundFunctional strong laws of large numbers for Euler characteristic processes of extreme sample clouds
Thomas, A. M., & Owada, T. (2021) · Extremes · doi:10.1007/s10687-021-00419-1 · Cited by 5
“A persistence barcode is a graphical descriptor of persistent homology, which allows us to visualize the birth time and death time of cycles (Ghrist 2008; Carlsson 2009).”
BackgroundStatistical Embedding: Beyond Principal Components
Tjøstheim, D., Jullum, M., & Løland, A. (2023) · Statistical Science · doi:10.1214/22-sts881 · Cited by 6
“See e.g. Carlsson (2009) for a precise definition.”
Background
Metadata
- Authors
- Carlsson, G.
- Affiliations
- Stanford University (US)
- Corresponding author
- Carlsson, Gunnar
- ORCID
- Carlsson, Gunnar
- Venue
- Bulletin of the American Mathematical Society
- Publisher
- American Mathematical Society (AMS)
- Published
- 2009-01-29
- Volume
- 46
- Issue
- 2
- Pages
- 255-308
- ISSN
- 0002-9904 (ISSN-L)
- 1088-9485
- 0273-0979
- OpenAlex
- W1991566301
- Cited by
- 2,302
- References
- 78
- Citations by year
- 2026: 215 · 2025: 216 · 2024: 158 · 2023: 176 · 2022: 159 · 2021: 215 · 2020: 227 · 2019: 151 · 2018: 163 · 2017: 132 · 2016: 128 · 2015: 95 · 2014: 99 · 2013: 65 · 2012: 46
- FWCI
- 33.43
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Bulletin of the American Mathematical Society (publishedVersion)
- Funders
- National Science Foundation (0354543)
- Defense Advanced Research Projects Agency
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Topological and Geometric Data Analysis
- Physical Sciences › Mathematics › Geometry and Topology › Geometric and Algebraic Topology
- Physical Sciences › Mathematics › Applied Mathematics › Geometric Analysis and Curvature Flows
- Keywords
- point clouds, distance measures, high-dimensional data, biological data, microarray data, high-throughput data
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-11, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-11 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review