The DOI matches its Crossref record; 77 of the 78 references it cites were found in a registry or an index; 15 of the 44 sources its claims cite were checked for support.
Topology and data
Open accessAbstract
An important feature of modern science and engineering is that data of various kinds is being produced at an unprecedented rate. This is so in part because of new experimental methods, and in part because of the increase in the availability of high powered computing technology. It is also clear that the nature of the data we are obtaining is significantly different. For example, it is now often the case that we are given data in the form of very long vectors, where all but a few of the coordinates turn out to be irrelevant to the questions of interest, and further that we don’t necessarily know which coordinates are the interesting ones. A related fact is that the data is often very high-dimensional, which severely restricts our ability to visualize it. The data obtained is also often much noisier than in the past and has more missing information (missing data). This is particularly so in the case of biological data, particularly high throughput data from microarray or other sources. Our ability to analyze this data, both in terms of quantity and the nature of the data, is clearly not keeping pace with the data being produced. In this paper, we will discuss how geometry and topology can be applied to make useful contributions to the analysis of various kinds of data. Geometry and topology are very natural tools to apply in this direction, since geometry can be regarded as the study of distance functions, and what one often works with are distance functions on large finite sets of data. The mathematical formalism which has been developed for incorporating geometric and topological techniques deals with point clouds, i.e. finite sets of points equipped with a distance function. It then adapts tools from the various branches of geometry to the study of point clouds. The point clouds are intended to be thought of as finite samples taken from a geometric object, perhaps with noise. Here are some of the key points which come up when applying these geometric methods to data analysis. • Qualitative information is needed: One important goal of data analysis is to allow the user to obtain knowledge about the data, i.e. to understand how it is organized on a large scale. For example, if we imagine that we are looking at a data set constructed somehow from diabetes patients, it would be important to develop the understanding that there are two types of the disease, namely the juvenile and adult onset forms. Once that is established, one of course wants to develop quantitative methods for distinguishing them, but the first insight about the distinct forms of the disease is key.
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Claims the paper makes
53 No basis given · 38 Own interpretation · 23 Own result · 17 No full text of the sources · 9 Supported · 5 Partly supported · 5 Sources not found · 1 Couldn’t confirm · 1 Not checked yet · accuracy not yet measured
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“Much of current research efforts are focused in this direction (see, e.g., [43] and [39]), and functoriality provides the right general mathematical framework for addressing them.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1. Introduction
What the sources say
- Could not verifycites [43] von Luxburg, Ulrike, 2008, Consistency of spectral clustering, Ann. Statist. sourceExperimental — accuracy not yet measured
The Introduction discusses questions about whether clustering algorithms converge, and Section 5.2 constructs operators for analyzing spectral clustering. Neither passage addresses functoriality or presents it as a framework for resolving ambiguities in clustering; searches for those concepts found no supporting passage in our copy. (LLM, conf=0.76)
- Not matchedcites [39] kleinbergJ.M. Kleinberg, An impossibility theorem for clustering, NIPS 2002: 446-453.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: unclear — It is unclear whether it could be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Could not verify
Every source the paper cites for this claim was checked and supports it.
In the paper
“We argue that, as suggested in [46], it has a role to play in the study of point cloud data as well, and we give two illustrations of how this could happen, within the context of clustering.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1. Introduction
What the sources say
- Supportedcites [46] McCullagh, Peter, 2002, What is a statistical model?, Ann. Statist. source
Section 8, “Extensive response variable” (page unavailable), describes a point process as a countable set of points and treats Poisson point-process models as natural transformations of functors. That supports the cited suggestion that functoriality could be useful for point-cloud data; the citing authors present clustering as their own proposed application. (LLM, conf=0.86)
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Niyogi, Smale, and Weinberger in [52] provide sufficient hypotheses which guarantee that this is possible for Riemannian manifolds.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.3. Persistent homology.
What the sources say
- Partly supportedcites [52] Niyogi, Partha, 2008, Finding the homology of submanifolds with high confidence from random samples, Discrete Comput. Geom. sourceExperimental — accuracy not yet measured
Section 2 (Preliminaries; page unavailable) considers compact Riemannian submanifolds sampled as data. Section 3 (An Outline of Our Main Results; page unavailable) gives conditions under which the samples recover the manifold’s homology with high confidence. It does not guarantee reconstruction of the manifold itself, so the claim is broader than the result. (LLM, conf=0.88)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“The result of this construction is a database M of ca.4.5 × 10 6 points on a 7-dimensional ellipsoid in R 8 .”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.4. Example: Natural image statistics.
What the sources say
- Partly supportedcites [41] lee A.B. Lee, K.S. Pedersen, and D. Mumford, The nonlinear statistics of high-contrast patches in natural images, International Journal of Computer Vision (54), No. 1-3, August 2003, pp. 83-103. sourceExperimental — accuracy not yet measured
Section 3 (Preprocessing; page unavailable) says the preprocessed points lie on a 7-dimensional ellipsoid in R⁹, which is transformed into a 7-dimensional sphere in R⁸. Section 2 (Optical and Range Data Sets; page unavailable) reports about 4.2 million optical patches, not approximately 4.5 million points in a database M. The source supports the general dimensional construction, but not the stated count and geometry together. (LLM, conf=0.85)
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Their method is to prove that the Čech complex associated to a covering by balls of a fixed radius is homotopy equivalent to the underlying manifold.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.3. Persistent homology.
What the sources say
- Supportedcites [52] Niyogi, Partha, 2008, Finding the homology of submanifolds with high confidence from random samples, Discrete Comput. Geom. source
Section 3 states that, when the sample is sufficiently dense and the ball radius meets the stated bound, the manifold is a deformation retract of the union of the balls. Section 3.1 constructs the nerve of that ball cover and invokes the Nerve Lemma because the balls are convex. Together, these support the claimed homotopy equivalence under the method’s conditions; the paper calls the nerve a simplicial complex rather than a Čech complex. (LLM, conf=0.88)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“However, it turns out that there is a classification theorem (see [64]) for a subcategory of the category of N-persistence F -vector spaces, where F is a field.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1=2
What the sources say
- Supportedcites [64] Zomorodian, Afra, 2005, Computing persistent homology, Discrete Comput. Geom. source
Section 3.1 (p. 11) establishes an equivalence between finite-type persistence modules and finitely generated graded modules over R[t]. Corollary 3.1 in Section 3.2 (p. 12) then classifies the isomorphism classes of finite-type persistence modules over a field F by finite sets of P-intervals. Finite-type modules are the subcategory referred to in the claim. (LLM, conf=0.94)
plain- Asserted: passes — The authors put it forward as true.
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- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“In [9], such a context is developed which is not dependent on the choice of a cost function but is rather dependent on”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
What the sources say
- Supportedcites [9] memolicarlsson G. Carlsson and F. Memoli, Persistent Clustering and a Theorem of J. Kleinberg , Preprint, March 2008. source
The Discussion (p. 16) presents persistence and functoriality as organizing principles and proposes defining clustering methods through sets of constraints, rather than a chosen cost function. The Results (p. 10) explicitly compares the framework’s properties with Kleinberg’s axiomatic conditions. (LLM, conf=0.87)
plain- Asserted: passes — The authors put it forward as true.
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- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“It can be shown that p Σ is a homotopy equivalence (see [65]).”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 4.3. Tree based persistence.
What the sources say
- Supportedcites [65] Zomorodian, Afra, 2008, Localized homology, Comput. Geom. source
Lemma 1 (global; page unavailable) states that projection from a blowup complex to the covered space is a homotopy equivalence when a simplicial complex is covered by subcomplexes. The paper uses π_X and X_U rather than pΣ and M(|Σ|,S), but states the projection result attributed to it. (LLM, conf=0.88)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“This notion of clustering is closely related to clique clustering algorithms in network clustering [53].”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
What the sources say
- Supportedcites [53] clique G. Palla, I. Derènyi, I. Farkas, and T. Vicsek, Uncovering the overlapping community structure of complex networks in nature and society, Nature, Volume 435, 9 June 2005, pp. 814-818. source
The paper defines network communities through connected sets of adjacent k-cliques (p. 2) and describes an algorithm that finds cliques and clusters them using a clique-overlap matrix (p. 3). It therefore supports the citation as an example of clique-based network clustering; the comparison to the citing paper’s equivalence relation E is the citing authors’ own framing. (LLM, conf=0.88)
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“There is a unique M gen -functorial persistent clustering algorithm Ξ which is scale invariant and which satisfies the requirement that Ξ(E) = P .”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
What the sources say
- Partly supportedcites [9] memolicarlsson G. Carlsson and F. Memoli, Persistent Clustering and a Theorem of J. Kleinberg , Preprint, March 2008. sourceExperimental — accuracy not yet measured
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
- It may state two separate claims as one. 39
- Probably not a substantial claim. 41
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Although clustering is clearly a very important part of data analysis, the ways in which it is formulated and implemented are fraught with ambiguities.In particular, the arbitrariness of various threshhold choices and lack of robustness are difficulties one confronts.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1. Introduction
What the sources say
- Partly supportedcites [43] von Luxburg, Ulrike, 2008, Consistency of spectral clustering, Ann. Statist. sourceExperimental — accuracy not yet measured
The Introduction describes clustering as widely used and says its consistency guarantees are unsatisfactory. The discussion following Proposition 24 shows that unnormalized spectral clustering can fail and that its reliability can be difficult to assess. These passages support the general concern about reliability, but they do not identify arbitrary threshold choices as a difficulty or establish a lack of robustness for clustering generally; no page numbers are available in our copy. (LLM, conf=0.82)
- Not matchedcites [39] kleinbergJ.M. Kleinberg, An impossibility theorem for clustering, NIPS 2002: 446-453.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“In particular, in [41], indications were found that the data was concentrated around an annulus.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.4. Example: Natural image statistics.
What the sources say
- Supportedcites [41] lee A.B. Lee, K.S. Pedersen, and D. Mumford, The nonlinear statistics of high-contrast patches in natural images, International Journal of Computer Vision (54), No. 1-3, August 2003, pp. 83-103. source
Section 5.1 describes the ideal-edge manifold using a circular orientation parameter and a bounded displacement parameter, giving it an annulus-like parametrization. Section 5.2 finds evidence that optical data points concentrate near that manifold. The paper does not use the word “annulus,” but the citing sentence is a reasonable, hedged paraphrase. (LLM, conf=0.82)
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“By using persistence, one is able to develop a systematic way of addressing such questions.That methodology has been described in [65].”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 4.3. Tree based persistence.
What the sources say
- Supportedcites [65] Zomorodian, Afra, 2008, Localized homology, Comput. Geom. source
Section 1.2 describes a method for localizing topological attributes relative to a cover. Section 4.4 gives the persistence algorithm that computes representatives of homology classes, supplying descriptions of those attributes. Together, these passages support the paraphrase about systematically addressing where features come from. (LLM, conf=0.90)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“The algorithm Ξ is the algorithm which associates to a finite metric space (X, d X ) the persistent set {π 0 (V R(X, ))} ≥0 .”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
What the sources say
- Supportedcites [9] memolicarlsson G. Carlsson and F. Memoli, Persistent Clustering and a Theorem of J. Kleinberg , Preprint, March 2008. source
Example 2.1 (Section 2, p. 3) associates a finite metric space with a persistent set whose blocks at scale r contain exactly the points connected by chains of distances at most r. Those blocks are the connected components of the Vietoris–Rips complex at that scale, as the claim describes. (LLM, conf=0.90)
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Also, results about barcodes under the general heading have now begun to appear (see [15] and [16]).”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Additional information
What the sources say
- Supportedcites [15] Cohen-Steiner, David, 2007, Stability of persistence diagrams, Discrete Comput. Geom. source
The Introduction (p. 2) says the paper makes concrete statements about the stability of barcodes. The Applications section (p. 16) states a Barcode Theorem, making this paper an example of the barcode results the citing sentence says have begun to appear. (LLM, conf=0.90)
- Not matchedcites [16] stabilitytwo D. Cohen-Steiner, H. Edelsbrunner, J. Harer and Y. Mileyko, Lipschitz functions have 𝐿_{𝑝}-stable persistence, Found. Comput. Math., to appear.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Supported
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“It is readily checked that θ is a functor from N pers (Ab) to the category of graded Z[t]-modules, and is in fact an equivalence of categories, since an inverse functor can be given by M * → {M n }, where the morphisms ψ mn are given by multiplication by t n-m .”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1=2
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A first observation is that the points are scattered throughout the 7-sphere, in the sense that no point on S 7 is very far from the set, but that the density appears to vary a great deal.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.4. Example: Natural image statistics.
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We note that there are a number of short lines, and one long one.According to the philosophy mentioned above, this suggests that the first Betti number should be estimated to be one.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.4. Example: Natural image statistics.
hedgedcorrelationalSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We note that in this case, there are many short segments and 5 longer segments.It is again the case that this barcode is stable over the choices of landmark points and samples from M. This suggests that the first Betti number of the putative underlying space should be five.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Primary circle
hedgedcorrelationalSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Note that while the two smaller holes have now been “filled in”, a new hole has been introduced in the lower right-hand portion of the figure.Consequently, if we were to compute the homology of this complex, we would obtain a first Betti number of two.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1=2
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“We note that the vertical circles intersect the horizontal primary circle in two points, and do not intersect each other, so the picture in question produces a natural embedding of the three circle model in the Klein bottle.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Three circle model in the data
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We note that the β 0 barcode (the upper picture) clearly shows the single component, the β 1 shows two lines from threshhold parameter values .15 to .35, and finally the β 2 barcode shows a single line on roughly the same interval.This gives β 0 = 1, β i = 2, and β 2 = 1.These are the mod 2 Betti numbers for the Klein bottle.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Three circle model in the data
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“At both scales, there is a central dense core, and two”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 3.5. Examples.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These two are distinguished by mod 3 homology, and we have performed the computation to show that the mod 3 homology is consistent with the Klein bottle and not with the torus.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Three circle model in the data
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We note that given only the Mapper output, one might suspect that the small feature (the array of orange nodes) could simply be an artifact, but examination of the data shows that they correspond to essentially distinct contact maps.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 3.5. Examples.
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Some experimentation with the three circle model results in the following picture in which the horizontal segments form the primary circle and the vertical segments form the secondary circles, as in the diagram.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Three circle model in the data
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We have informally confirmed that the indicated patches are the ones which occur in the high density portions of the data set.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Three circle model in the data
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“By far the most frequently occurring signatures were (1, 1, 0) and (1, 0, 1), corresponding to a circle and a sphere, respectively.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.5. Example: Electrode array data from primary visual cortex.
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Note that the two secondary circles each intersect the primary circle in two points, and that they do not intersect each other.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Three circle model in the data
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Note also that the data are obtained by simply examining the states occurring in the simulation, and that it does not include any dynamic information which would show how the states are traversed in the folding process.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 3.5. Examples.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The set S exhibits the three circle model clearly, but enlarging it still does not exhibit the non-trivial β 2 which would be characteristic of the Klein bottle.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Three circle model in the data
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“It is easy to check (a) that the factorization is a homeomorphism and (b) that the orbit space is homeomorphic to a Klein bottle.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Three circle model in the data
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“A M iso -functorial clustering algorithm determines an choice p ι ∈ P G ι ι for every ι, and conversely an arbitrary choice of such p ι 's determines an M iso -functorial clustering algorithm.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Each of these notions creates the structure of a category whose objects are the finite metric spaces, since one can readily observe that each of the classes of morphisms is closed under composition and contains the identity.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Some work in clustering theory has been done in trying to determine the optimal choice of , but it is now well understood that it is much more informative to maintain the entire dendrogram of the set, which provides a summary of the behavior of clustering under all possible values of the parameter at once.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1. Introduction
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In the biological context, notions of distance are constructed using some intuitively attractive measures of similarity (such as BLAST scores or their relatives), but it is far from clear how much significance to attach to the actual distances, particularly at large scales.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1. Introduction
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Topology studies only properties of geometric objects which do not depend on the chosen coordinates, but rather on intrinsic geometric properties of the objects.As such, it is coordinate-free.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1. Introduction
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Moreover, it is understood that most of the information about topological spaces can be obtained through diagrams of discrete sets, via a process of simplicial approximation.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1. Introduction
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We then demonstrate that persistence can be generalized in several different directions, providing more structure and information about the data sets in question.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Example.
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“If the clusterings are consistent, i.e. if the clusters in C(X 1 ) and C(X 2 ) in C(X 1 ∪ X 2 ) correspond well under these maps, then one can regard that as evidence that the subsample clusterings actually correspond to clusterings on the full data set X.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Example.
hedgedcorrelationaln = 2- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“For any topological space X with a finite number of path components, β 0 (X) is the number of path components.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Example.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“We begin with a discussion of persistent homology, which is a mathematical formalism which permits us to infer topological information from a sample of a geometric object, and we show how it can be applied to particular data sets arising from natural image statistics and neuroscience.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Example.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Functoriality is central in algebraic topology in that the functoriality of homological invariants is what permits one to compute them from local information, and that functoriality is at the heart of most of the interesting applications within mathematics.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1. Introduction
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We then show that the philosophy of functoriality can be used to reason about the nature of clustering methods, and we conclude by speculating about theorems one might hope to prove and discussing how the subject might develop more generally.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Example.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“One important goal of data analysis is to allow the user to obtain knowledge about the data, i.e. to understand how it is organized on a large scale.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1. Introduction
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Although we often receive data in the form of vectors of real numbers, it is frequently the case that the coordinates, like the metrics mentioned above, are not natural in any sense, and that therefore we should not restrict ourselves to studying properties of the data which depend on any particular choice of coordinates.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1. Introduction
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The second method is more efficient, since it is precisely adapted to the complex at hand.Furthermore, it contains complete information about the original R-vector space.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1=2
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In interpreting the output, one now finds that long intervals in the output barcode indicate the presence of a homology class which “persists” over a long range of parameter values, while short intervals indicate cycles which are “born” at a given parameter value and then “die” at a nearby parameter value.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1=2
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“This is due to the fact that for finite metric spaces, it is generically the case that each value of the distance is taken only for one pair of points, so one does not have any points which are equidistant between a pair of landmarks.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.2. Building coverings and complexes.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Intuitively, we regard these two holes as coming from faulty sampling or other errors in the recovery of the data.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1=3
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The pictures and discussion above allow us to formulate the intuition that long intervals correspond to large scale geometric features in the space and that short intervals correspond to noise or inadequate sampling.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1=2
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Both the the Čech and Vietoris-Rips complexes typically produce simplices in dimensions much higher than the dimension of the space.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.2. Building coverings and complexes.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“It turns out that H simp k (X, Z) is always canonically isomorphic to the singular homology of the space |X|.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Remark.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The conclusion is that for simplicial complexes, homology is algorithmically computable, although standard methods for computing Smith normal form can often be painfully slow.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Remark.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“If further is sufficiently small, then all the balls will be geodesically convex, and the complex will compute the homology of M correctly.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.3. Persistent homology.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Note that if the centers actually lie on a submanifold M ⊆ R n , and the set X is sufficiently dense in M , then this complex is the Čech complex attached to a covering of M .”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.3. Persistent homology.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“It is clearly the case that if we have 0 ≤ ≤ , then we have an inclusion W s (X, L, ) ↩→ W s (X, L, ) and similarly for W s V R , W w , and W w V R .”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.2. Building coverings and complexes.
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, for finite metric spaces, it generically produces degenerate (i.e.discrete) complexes, with no one dimensional simplices.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.2. Building coverings and complexes.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In rough terms, δ k for large values of k computes density using points in large neighborhoods of x, and for small values uses small neighborhoods.So, δ k for large k corresponds to a smoothed out notion of density, and for small k corresponds to a version which carries more of the detailed structure of the data set.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.4. Example: Natural image statistics.
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“It was observed in [38] that in an informal sense, the images in the different conditions appeared to be quite similar, and a statistical analysis strongly suggested that the behavior of V1 in the spontaneous condition was consistent with a behavior which consisted in moving through a family of evoked images corresponding to responses to angular boundaries, without any particular order.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.5. Example: Electrode array data from primary visual cortex.
What the sources say
- Not checked — no full text of the sourcecites [38] kenet T. Kenet, D. Bibitchkov, M. Tsodyks, A. Grinvald, and A. Arieli, Spontaneously emerging cortical representations of visual attributes, Nature 425 (2003), 954-956. source
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“This result is consistent with what was obtained by using ISOMAP in [61].”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 3.5. Examples.
What the sources say
- Not checked — no full text of the sourcecites [61] isomapJ.B. Tenenbaum, V. de Silva and J.C. Langford, A global geometric framework for nonlinear dimensionality reduction, Science 290 (2000) (December), pp. 2319-2323. source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The arrays of neurons studied in [59] are from the primary visual cortex or V1 in Macaque monkeys.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.5. Example: Electrode array data from primary visual cortex.
What the sources say
- Not checked — no full text of the sourcecites [59] visionG. Singh, F. Memoli, T. Ishkhanov, G. Carlsson, G. Sapiro and D. Ringach, Topological Structure of Population Activity in Primary Visual Cortex, Journal of Vision, Volume 8, Number 8, Article 11, pp. 1-18, 2008. source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“since the intuition is that features which appear at several levels in such a multiresolution diagram would be more intrinsic to the data set than those which appear at a single level.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Definition 3.2.
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The core consists of normal or near-normal patients, and the two flares consist of patients with the two different forms of diabetes.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 3.5. Examples.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Z[m, n]-persistence vector spaces are examples of quiver representations, a highly developed area of algebra.See [29] for a complete description.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Corollary 4.6.
What the sources say
- Not checked — no full text of the sourcecites [29] Gabriel, P., 1997 source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These functions are clearly invariants of the isomorphism class of the module M , and we refer to them as the dimension and rank invariants, respectively.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Definition 4.3.
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“If the question is instead whether or not an element arises from a union of k elements of S, then we do not obtain complete information this way, but we do obtain partial information in that we can preclude the possibility that a class arises from such a union.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 4.3. Tree based persistence.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“We note that the correspondence X → π 0 (X) can actually be viewed as a functor (see [44]) from the category of topological spaces to the category of sets, in the sense that a continuous map f : X → Y induces a map of sets π 0 (f ) : π 0 (X) → π 0 (Y )”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
What the sources say
- Not matchedcites [44] Mac Lane, Saunders, 1998
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As we have seen above, algebraic topology is capable of producing signatures which indicate the presence of topological features within a space.As it stands, however, it is not capable of describing the source of the feature, i.e.where in the space the hole or other feature is located.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 4.3. Tree based persistence.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Theorem 4.5.Let M denote any Z[m, n]-persistent F -vector space, so that every vector space V i is finite dimensional.Then there is an isomorphism”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Example.
What the sources say
- Not checked — no full text of the sourcecites [29] Gabriel, P., 1997 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“As Kleinberg points out, it is possible to do so in a number of different ways by specifying cost functions on particular clusterings, and prove a uniqueness result for optimal choices of clusterings with respect to this cost function”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
What the sources say
- Not matchedcites [39] kleinbergJ.M. Kleinberg, An impossibility theorem for clustering, NIPS 2002: 446-453.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“This machinery is not available in the multivariable case, but it turns out that it can be replaced by the Gröbner basis methodology (see [18] or [47]).”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Definition 4.3.
What the sources say
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Any finite Z-persistence F -vector space M can be decomposed as”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Corollary 4.6.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The properties of trees guarantee that ≤ T is a partial ordering.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 4.3. Tree based persistence.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
In the paper
“In the paper [59], this was shown by simulation, which is of course always an option.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
What the sources say
- Not checked — no full text of the sourcecites [59] visionG. Singh, F. Memoli, T. Ishkhanov, G. Carlsson, G. Sapiro and D. Ringach, Topological Structure of Population Activity in Primary Visual Cortex, Journal of Vision, Volume 8, Number 8, Article 11, pp. 1-18, 2008. source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Not only is the correspondence π 0 (-) a functor from spaces to sets, it is equipped with a natural surjective map of sets η X : X → π 0 (X).”
- Also stated in: § 5. Reasoning about clustering · plain
“Further, these surjective maps have the property that for every continuous map f : X → Y , the diagram of sets (below) commutes.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
- Also stated in: § 5. Reasoning about clustering · plain
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“While the observation is of course suggestive, to prove that it indicates structure in the data set one must show that segments of the indicated length cannot occur in a random model associated to a null hypothesis.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This means that Kleinberg's conditions also make sense in this context.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
In the paper
“We point out that in the context of ordinary homology (i.e.not persistent homology), M. Penrose and others have developed a theory of “geometric random graphs” and have proved various results concerning the Betti numbers of complexes attached to such graphs (see [54]).”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Additional information
What the sources say
- Not checked — no full text of the sourcecites [54] Penrose, Mathew, 2003 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It is clear that a C-functorial clustering algorithm is a clustering algorithm in the sense of Kleinberg, since the surjective map of sets from X to χ(X, d X ) yields a partition of X, namely the partition whose blocks are the sets η -1 X (z), as z ranges over all elements of χ(X, d X ).”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
None of the sources cited for this claim have been checked yet.
In the paper
“Each such interval can be considered as a point in the set D = {(x, y) ∈ R 2 |x ≤ y}, and the output is therefore a finite subset of the set D. As such, we now have a finite spatial point process (see [20], [3]).”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Additional information
What the sources say
- Not matchedcites [20] Daley, D. J., 1988
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked — no full text of the sourcecites [3] Baddeley, Adrian, 2007, Spatial point processes and their applications source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
The paper relies on this without support.
There is no classification theorem for R-persistence abelian groups.
“However, we do not have such a theorem.”
The paper relies on this without support.
There is still no classification theorem for graded Z[t]-modules.
“Now, there is still no classification theorem for graded Z[t]-modules.”
The paper relies on this without support.
In studying data from experiments, one typically cannot assume that the data lies along a submanifold.
“If one is interested in studying data from experiments, though, one typically cannot assume that the data lies along a submanifold.”
The paper relies on this without support.
Even if one could assume that the data lies along a manifold, one is usually not in a position to verify that the stringent hypotheses of [52] are satisfied.
“Further, even if one could assume that the data lies along a manifold, one is usually not in a position to verify that the stringent hypotheses of [52] are satisfied.”
The paper relies on this without support.
The analysis of populations of neurons is amenable to geometric analysis.
“The second problem appears to be very amenable to geometric analysis, since it will involve the activities of several neurons at once.”
The paper relies on this without support.
The mechanism by which V1 carries out low-level tasks such as edge and line detection is not understood.
“However, the mechanism by which it carries out these tasks is not understood.”
The paper relies on this without support.
The classification of finitely generated modules over multivariable polynomial rings is much more complicated than the corresponding result for single-variable polynomial rings, and no reasonable parametrization is known.
“As is well known to algebraists, the classification of finitely generated modules over multivariable polynomial rings is much more complicated than the corresponding result for single variable polynomial rings, and in fact no reasonable parametrization is known.”
The paper relies on this without support.
In the case of more than one variable, the classification of multigraded modules depends on the field in question.
“examples show that in the case of more than one variable, the classification of multigraded modules definitely depends on the field in question.”
The paper relies on this without support.
The classification in the multivariable case is parametrized by points in moduli varieties over the ground field.
“In fact, the classification in the multivariable case is parametrized by points in moduli varieties over the ground field”
The paper relies on this without support.
Hierarchical clustering algorithms do not report single partitions but rather dendrograms, which are roughly speaking R+-persistent sets.
“This is not an unreasonable thing to do in view of the fact that hierarchical clustering algorithms do not report single partitions but rather dendrograms, which are roughly speaking R + -persistent sets.”
Citations
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“We note that the correspondence X → π0(X) can actually be viewed as a functor (see [44]) from the category of topological spaces to the category of sets, in the sense that a continuous map f : X → Y induces a map of sets π0(f) : π0(X)→ π0(Y ), satisfying certain obvious conditions on composite maps and identity maps.”
BackgroundMethodDensity Estimation for Statistics and Data Analysis
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Cited by
Sorted by citations
Items 1,501–1,600 of 2,331
Temporal and Spatial Analyses of TB Granulomas to Predict Long-Term Outcomes
Joslyn, L. R., Renardy, M., Weissman, C. R., et al. (2020) · Springer eBooks · doi:10.1007/978-3-030-56510-7_15 · Cited by 2
Combining Geometric and Topological Information for Boundary Estimation
Luo, H., & Strait, J. (2021) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata52589.2021.9672024 · Cited by 2
“It is not immediately clear how shape analysis methods [4, 5, 7] and topology-based methods [6, 8, 9] can be combined together to accomplish this subtask.”
MethodInfluentialCoarse-refinement dilemma: On generalization bounds for data clustering
Vaz, Y., de Mello, R. F., & Grossi, C. H. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115399 · Cited by 2
Computational Topology and its Applications in Geometric Design
Lin, H., Dong, Z., & Chen, J. (2021) · Recent Patents on Engineering · doi:10.2174/1872212115666210901124742 · Cited by 2
0-Dimensional Persistent Homology Analysis Implementation in Resource-Scarce Embedded Systems
Branco, S., Carvalho, J. G., Reis, M. S., Lopes, N. V., & Cabral, J. (2022) · Sensors · doi:10.3390/s22103657 · Cited by 2
“problems into their main features, which turn the increasingly used high-dimensional data into easy-to-see and easy-to-understand problems [6,7].”
BackgroundYin, J., & Wang, Y. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.104396 · Cited by 2
Comparison of Word Embeddings of Unaligned Audio and Text Data Using Persistent Homology
Yessenbayev, Z., & Kozhirbayev, Z. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20980-2_59 · Cited by 2
Homology-Separating Triangulated Euler Characteristic Curve
Malott, N. O., Lewis, R. R., & Wilsey, P. A. (2022) · Proceedings · doi:10.1109/icdm54844.2022.00136 · Cited by 2
“Topological Data Analysis (TDA) treats a point cloud as a sampling of a topological space and characterizes the data based on its topological invariants [1].”
BackgroundTopological Data Analysis for Directed Dependence Networks of Multivariate Time Series Data
Yaagoubi, A. E., & Ombao, H. (2023) · Springer eBooks · doi:10.1007/978-981-99-0803-5_17 · Cited by 2
Ni, F., Zhao, Z., Li, R., & Zhang, H. (2023) · IEEE Access · doi:10.1109/access.2023.3309418 · Cited by 3
“complex, high-dimensional data [28], [29].”
Interleaving by Parts: Join Decompositions of Interleavings and Join-Assemblage of Geodesics
Kim, W., Mémoli, F., & Stefanou, A. (2023) · Order · doi:10.1007/s11083-023-09643-9 · Cited by 2
“This enables us to define an interleaving distance between objects in the category, which encompasses many distances in TDA and facilitates a uniform treatment of those distances.”
InfluentialExploring Graph and Digraph Persistence
Bergomi, M. G., & Ferri, M. (2023) · Algorithms · doi:10.3390/a16100465 · Cited by 2
Graph theoretic methods for the analysis of data in developing systems
Green, K. H., & Ricca, B. (2014) · Quality & Quantity · doi:10.1007/s11135-014-0089-5 · Cited by 2
“Although we have treated links and gaps equally, Carlsson (2009) noted that when there is no natural measurement of distance, then the use of that distance in analysis is arbitrary, and may not be appropriate for the data. Hence, the examination of links-gaps plots, and the corresponding edit distances, indicates that…”
BackgroundInfluentialPersistence intervals of fractals
Máté, G., & Heermann, D. W. (2014) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2014.03.037 · Cited by 2
THE APPLICATION OF THE EXPECTATION MAXIMIZATION ALGORITHM ONTO BIG DATA
Beffel, J. (2015) · Carroll Collected (John Carroll University) · Cited by 2
The ‘Life Machine’: A Quantum Metaphor for Living Matter
Rasetti, M. (2016) · International Journal of Theoretical Physics · doi:10.1007/s10773-016-3177-6 · Cited by 2
SE and SNL diagrams: Flexible data structures for MIR
McGuirl, M. R., Kinnaird, K. M., Savard, C., & Bugbee, E. H. (2018) · International Symposium/Conference on Music Information Retrieval · doi:10.5072/zenodo.251658 · Cited by 2
“Topological summaries, such as the number of loops in the data, are then computed for each parameter value in the sequence [4, 5, 7].”
BackgroundMethodDetection of the homotopy type of an object using differential invariants of an approximating map
Курочкин, С. В. (2019) · Computer Optics · doi:10.18287/2412-6179-2019-43-4-611-617 · Cited by 2
Approximating lower-star persistence via 2D combinatorial map simplification
Damiand, G., Paluzo-Hidalgo, E., Slechta, R., et al. (2020) · Pattern Recognition Letters · doi:10.1016/j.patrec.2020.01.018 · Cited by 2
“See Dey et al. (1999); Bern et al. (1999); Carlsson (1999) for initial reports and Edelsbrunner and Harer (2010) for a modern exposition of the field.”
DCG++: A data-driven metric for geometric pattern recognition
Guan, J., Hsieh, F., & Koehl, P. (2019) · PLoS ONE · doi:10.1371/journal.pone.0217838 · Cited by 2
A hierarchy of dismantlings in graphs
Fieux, E., & Jouve, B. (2020) · Discrete Mathematics · doi:10.1016/j.disc.2020.111914 · Cited by 2
“The study of simplicial complexes appears today in a very wide spectrum of research and applications [8, 14, 27].”
BackgroundMiao, R., Yang, Z., & Gavrishchaka, V. (2020) · International Conference on Information and Computer Technologies (ICICT) · doi:10.1109/icict50521.2020.00025 · Cited by 2
“Therefore, intuitively, topological comparison of two point clouds should involve properties of sequence of simplicial representations as formalized in persistent homology framework [20-26].”
BackgroundInfluentialMerkt, W., Ivan, V., Dinev, T., et al. (2021) · IEEE Transactions on Robotics · doi:10.1109/tro.2021.3069132 · Cited by 2
“The theory behind this approach has been studied in [23], and computationally efficient algorithms have been proposed in [24].”
MethodJoint Geometric and Topological Analysis of Hierarchical Datasets
Aloni, L., Bobrowski, O., & Talmon, R. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-86523-8_29 · Cited by 2
“In this work, we propose to combine manifold learning and TDA in order to provide informative representations of high-dimensional data, under the assumption that they can be arranged into several hierarchical datasets as follows.”
BackgroundInfluentialTopological Data Analysis For Evaluating PDE-based Denoising Models
Al-Jaberi, A. K., & Hameed, E. M. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1897/1/012006 · Cited by 2
“There are several of challenges when deal with Big Data applications, as introduced in [14], [15] and [16].”
BackgroundWaliszewski, P., & Banaszak, G. (2021) · International Conference on Control Systems and Computer Science · doi:10.1109/cscs52396.2021.00042 · Cited by 2
Blevins, A. S., & Bassett, D. S. (2021) · Springer eBooks · doi:10.1007/978-3-319-57072-3_87 · Cited by 2
Topological Correlation of Brain Signals
Yin, J., & Wang, Y. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9747824 · Cited by 2
Distributed computation of homology using harmonicsPreprint
Chintakunta, H., & Krim, H. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1306.1158 · Cited by 2
“In addition, algebraic topology has seen useful applications in topological analysis of data [3,13,17,20], where the data is available as a point cloud and the topology of the underlying manifold is of interest.”
BackgroundNecessary conditions for discontinuities of multidimensional size functions
Cerri, A., & Frosini, P. (2009) · AMS Acta (University of Bologna) · doi:10.6092/unibo/amsacta/2625 · Cited by 2
Coarse Ricci curvature with applications to manifold learning problemPreprint
Ache, A. G., & Warren, M. W. (2014) · arXiv (Cornell University) · Cited by 2
Varlets: Additive Decomposition, Topological Total Variation, and Filtering of Scalar Fields.Preprint
Brooks, M. (2015) · arXiv (Cornell University) · Cited by 2
Min Morse: Approximability & ApplicationsPreprint
Rathore, A. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1503.03170 · Cited by 2
A presentation of general multipersistence modules computable in polynomial time?Preprint
Patriarca, A., Scolamiero, M., & Vaccarino, F. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1210.1932 · Cited by 2
Cortical Geometry Network and Topology Markers for Parkinson's DiseasePreprint
Garg, A., Lu, D., Popuri, K., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.04393 · Cited by 2
“tances whose alterations with disease are of interest. Thus, in order to overcome such scale-related dierences, we normalize the values of the interpatch distance for each individual to the range of [0;1] prior to the generation of a graph ltration. This enables a comparison of the network and topology features across…”
BackgroundMethodInfluentialRandom \v{C}ech Complexes on Riemannian ManifoldsPreprint
Bobrowski, O., & Oliveira, G. (2017) · arXiv (Cornell University) · Cited by 2
Improved Approximate Rips Filtrations with Shifted Integer LatticesPreprint
Choudhary, A., Kerber, M., & Raghvendra, S. (2017) · arXiv (Cornell University) · doi:10.4230/lipics.esa.2017.28 · Cited by 2
“Persistent homology [4, 9, 11] is a technique to analyze of data sets using topological invariants.”
MethodParallel multi-scale reduction of persistent homology filtrationsPreprint
Mendoza-Smith, R., & Tanner, J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1708.04710 · Cited by 2
“Knowledge of the homologies persistent in a dataset can aid interpretation of the data, see for example [3, 4, 5, 6, 14, 22, 18].”
BackgroundPersistence Flamelets: multiscale Persistent Homology for kernel density explorationPreprint
Padellini, T., & Brutti, P. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1709.07097 · Cited by 2
“In recent years there has been noticeable interest in the study of the “shape of data” [2].”
BackgroundInterleaving Distance as a LimitPreprint
Meehan, K., & Meyer, D. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1710.11489 · Cited by 3
“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…”
MethodConvergence of Hierarchical Clustering and Persistent Homology Methods on Directed NetworksPreprint
Chowdhury, S., & Mémoli, F. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.04211 · Cited by 2
Evolutionary homology on coupled dynamical systemsPreprint
Cang, Z., Munch, E., & Wei, G. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.04677 · Cited by 1
“The interested reader can find further specifics in, e.g., Carlsson (2009), or Edelsbrunner and Harer (2010).”
BackgroundConvergence of Persistence Diagrams for Topological CracklePreprint
Owada, T., & Bobrowski, O. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.01602 · Cited by 2
“We suggest [13, 21] as a good introductory reading, while a more rigorous coverage of algebraic topology is in [23].”
MethodPersistence modules, symplectic Banach-Mazur distance and Riemannian metricsPreprint
Stojisavljević, V., & Zhang, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.11151 · Cited by 2
“For a detailed exposition see [22, 27, 14, 63, 11, 21, 41, 15, 45].”
HELOC Applicant Risk Performance Evaluation by Topological Hierarchical DecompositionPreprint
Brown, K., Doran, D., Kramer, R., & Reynolds, B. F. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.10658 · Cited by 9
“A good overview of TDA is given by Carlsson [2].”
MethodPersistence Modules on Commutative Ladders of Finite TypePreprint
Escolar, E. G., & Hiraoka, Y. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1404.7588 · Cited by 2
“(3.2) As explained in Section 1, persistence modules on the commutative ladder CL(fb) frequently arise in TDA.”
BackgroundInfluentialLinear-Size Approximations to the Vietoris-Rips FiltrationPreprint
Sheehy, D. R. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1203.6786 · Cited by 2
“See also the survey by Carlsson for background on the topological view of data [3].”
Persistent homology analysis of brain artery treesPreprint
Bendich, P. L., Marron, J. S., Miller, E., Pieloch, A., & Skwerer, S. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1411.6652 · Cited by 5
“Edelsbrunner and Harer (2010) and Carlsson (2009) are good background sources for TDA.”
BackgroundPersistence Terrace for Topological Inference of Point Cloud Data
Moon, C., Giansiracusa, N., & Lazar, N. A. (2018) · Figshare · doi:10.6084/m9.figshare.5758542 · Cited by 2
Anand, D. V., Xia, K., & Mu, Y. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.06171 · Cited by 2
Topological Data Analysis of Single-cell Hi-C Contact MapsPreprint
Carrière, M., & Rabadán, R. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/486936 · Cited by 5
Topological inference from spontaneous activity structures in FMRI videos with peristence barcodesPreprint
Don, A. P. H., Peters, J. F., Ramanna, S., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/809293 · Cited by 2
“A common approach is to build a continuous shape (graphs) on top of data to detect complex topological and underlying geometric structures [5, 7].”
BackgroundIntrinsic Topological Transforms via the Distance Kernel EmbeddingPreprint
Maria, C., Oudot, S., & Solomon, E. (2019) · arXiv (Cornell University) · doi:10.4230/lipics.socg.2020.56 · Cited by 2
“s in applied topology, namely those pertaining to persistence modules and persistence diagrams. For an introduction to these topics, the reader can consult the articles of Ghrist [Ghr08] and Carlsson [Car09]. A more formal and comprehensive treatment can be found in the texts by Edelsbrunner and Harer [EH10], Ghrist […”
InfluentialDissecting Ethereum Blockchain Analytics: What We Learn from Topology and Geometry of Ethereum GraphPreprint
Li, Y., Islambekov, U., Akçora, C. G., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.10105 · Cited by 2
“Vietoris-Rips is a widely used simplicial complex due to its easy construction and fast computational implementation [8].”
BackgroundPersistent Homotopy Groups of Metric SpacesPreprint
Mémoli, F., & Zhou, L. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.12399 · Cited by 2
Topological representation of the geometry of metric spacesPreprint
Joharinad, P., & Jost, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.10262 · Cited by 2
“the unit simplex in RI with its Euclidean topology, then Xˇ becomes a topological space. When I is finite, with k+1 elements, we also call the resulting simplex a k-simplex. Remark. • As mentioned in [11], this construction is computationally expensive since it requires the storage of simplices of various dimensions. •…”
BackgroundMethodInfluentialManifold structure in graph embeddingsPreprint
Rubin‐Delanchy, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.05168 · Cited by 4
“1 Introduction The hypothesis that high-dimensional data tend to live near a manifold of low dimension is an important theme of modern statistics and machine learning, often held to explain why high-dimensional learning is realistically possible [66, 6, 17, 15, 21, 47].”
BackgroundHomological CoordinatizationPreprint
Tausz, A., & Carlsson, G. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1107.0511 · Cited by 2
Self-citationAlgebraic TopologyPreprint
Robins, V. (2013) · arXiv (Cornell University) · Cited by 2
Topology in cyber researchPreprint
Huntsman, S., Palladino, J., & Robinson, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.03299 · Cited by 2
“Perhaps the most widely recognized and utilized tool in TDA is persistent homology [70, 26, 9, 23, 27, 57].”
MethodA Fast and Robust Method for Global Topological Functional OptimizationPreprint
Solomon, E., Wagner, A., & Bendich, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.08496 · Cited by 2
Algorithmic decomposition of filtered chain complexesPreprint
Chachólski, W., Giunti, B., Jin, A., et al. (2020) · arXiv (Cornell University) · Cited by 2
Multidimensional Persistence Module Classification via Lattice-Theoretic ConvolutionsPreprint
Riess, H., Hansen, J., & Ghrist, R. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.14057 · Cited by 2
“For a primer on persistent homology, see [10, 6]; for multiparameter persistent homology, see [16].”
BackgroundClassification based on Topological Data AnalysisPreprint
Kindelan, R., Frías, J., Cerda, M., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.03709 · Cited by 2
“[8] strengthened the mathematical foundations and also proposed another visualization tool called persistence Barcodes [19, 8].”
BackgroundMethodInfluentialOnline unsupervised deep unfolding for MIMO channel estimationPreprint
Magoarou, L. L., & Paquelet, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.14615 · Cited by 5
“Data processing techniques are often based on the manifold assumption : Meaningful data (signals) lie near a low dimensional manifold, although their apparent dimension is much larger [1], [2].”
BackgroundTopological Detection of Trojaned Neural NetworksPreprint
Zheng, S., Zhang, Y., Wagner, H., Goswami, M., & Chen, C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.06469 · Cited by 20
“Our method uses tools from topological data analysis, particularly persistent homology [21, 6].”
MethodMoon, C., Li, Q., & Xiao, G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.12102 · Cited by 2
“Persistent homology is a commonly used topological data analysis tool that analyzes the shape of data with the multi-scale topological lens (Edelsbrunner and Harer, 2008; Carlsson, 2009).”
MethodBuilding Information Filtering Networks with Topological Constraints: Algorithms and Applications
Massara, G. P. (2020) · UCL Discovery (University College London) · Cited by 2
Persistent Cup-LengthPreprint
Contessoto, M., Mémoli, F., Stefanou, A., & Zhou, L. (2021) · arXiv (Cornell University) · doi:10.26092/elib/4278 · Cited by 2
Accessing United States Bulk Patent Data with patentpy and patentrPreprint
Yu, J. T., Beltz, H., Desai, M. Y., Érdi, P., Scott, J. G., & Wadhwa, R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.08481 · Cited by 2
A Topological Perspective on Weather RegimesPreprint
Strømmen, K., Chantry, M., Dorrington, J., et al. (2021) · Research Square · doi:10.21203/rs.3.rs-879214/v1 · Cited by 2
Johnson, M., & Jung, J.-H. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.09218 · Cited by 2
“Topological Data Analysis (TDA) is rising field useful for the analysis of high-dimensional data structure [1].”
BackgroundEmerging vaccine-breakthrough SARS-CoV-2 variantsPreprint
Wang, R., Chen, J., Hozumi, Y., Yin, C., & Wei, G. (2021) · PubMed · doi:10.48550/arxiv.2109.04509 · Cited by 7
“Algebraic topology [42,43] has achieved tremendous success in many fields including biochemical and biophysical properties [44].”
BackgroundTopological Structure of Population Activity in Mouse Visual Cortex Encodes Visual Scene RotationsPreprint
Beshkov, K., & Einevoll, G. T. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.02.13.528247 · Cited by 2
“Topological data analysis, is a rapidly growing field which adapts the computational methods developed for the classification of topological spaces in the late 19th century to the data rich world of today [73, 74, 75].”
BackgroundA topological perspective on the dual nature of the neural state space and the correlation structurePreprint
Vaupel, M., Hermansen, E., & Dunn, B. A. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.17.562775 · Cited by 2
“This pipeline consists of combining the fuzzy topological representation constructed in the first step of UMAP [5] with persistent cohomology [11, 30].”
MethodYoon, I. H. R., Jenkins, R. P., Colliver, E., Zhang, H., Novo, D., Moore, D. A., Ramsden, Z., Rullan, A., Fu, X., Yuan, Y., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.01.05.574362 · Cited by 2
FinTDA: Python package for estimating market change through persistent homology diagrams
Souto, H. G., Baris, I., Heuvel, S. K., et al. (2024) · Software Impacts · doi:10.1016/j.simpa.2024.100637 · Cited by 2
Signature Distance: Generalizing Energy StatisticsPreprint
Lazzaro, N., Marchesi, R., Leonardi, G., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.23.619602 · Cited by 2
“Topological data analysis (Carlsson, 2009; Edelsbrunner and Harer, 2010) captures multi-scale geometric structure via filtrations but produces summaries that are expensive to compare natively.”
Moroke, N. D. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6418314 · Cited by 3
2-dimensional stratifoldsPreprint
Gómez-Larrañaga, J. C., González-Acuña, F., & HEIL, W. H. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1505.03188 · Cited by 1
Topological Data Analysis of Potentiometric Multisensor Measurements in Treated Wastewater
Belikova, V., Владимирович, П. В., Legin, E., Melenteva, A., Legin, A. V., & Kirsanov, D. O. (2018) · Journal of Analysis and Testing · doi:10.1007/s41664-018-0066-4 · Cited by 1
“Recently, topological data analysis (TDA) was introduced in literature as a new approach to handle big data [6].”
BackgroundFinding the homology of manifolds using ellipsoids
Kališnik, S., & Lešnik, D. (2023) · Journal of Applied and Computational Topology · doi:10.1007/s41468-023-00145-6 · Cited by 2
“The value p in the definition of ellipsoids serves as a “persistence parameter” (Ghrist 2008; Carlsson 2009; Carlsson and Zomorodian 2005; Edelsbrunner et al. 2002; Breiding et al. 2018).”
Lee, D., Bresten, C., Youm, K., et al. (2020) · International Journal of Modern Physics C · doi:10.1142/s012918312050117x · Cited by 1
Topological Estimation of 2D Image Data via Subsampling and Application to Firn
Belotti, V., Jester, M., Keegan, K., et al. (2021) · IEEE Access · doi:10.1109/access.2021.3050377 · Cited by 1
Bayesian Estimation of Topological Features of Persistence Diagrams
Martínez, A. F. (2022) · Bayesian Analysis · doi:10.1214/22-ba1341 · Cited by 1
“The method was introduced by [17] and its theory has been further considered in, for example, [8], [16], [26], [46] and [64].”
MethodHasan, Z. A., & Gobithaasan, R. U. (2023) · Communications in computer and information science · doi:10.1007/978-981-99-0405-1_19 · Cited by 2
A tensor product approach to non-local differential complexes
Hinz, M., & Kommer, J. (2023) · Mathematische Annalen · doi:10.1007/s00208-023-02703-w · Cited by 1
“Applications of related theories to data analysis, [20, 23, 36], sparked new interest in scaled differential complexes and cohomologies on metric spaces, [8, 42, 88]; ideas had been sketched earlier in [78, 79].”
BackgroundCompact Boundary Matrix from a Polytopal Complex for Computing Persistent Homology
Singh, R. P., & Wilsey, P. A. (2023) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata59044.2023.10386348 · Cited by 1
“Topological Data Analysis (TDA) is a technique in data science to understand data by its topological invariants [1], [2].”
MethodTopological Insights Into Weather Dynamics in the Indian Context
Azarudheen, S., Menezes, D. J., & Clements, V. (2024) · Advances in geospatial technologies book series · doi:10.4018/979-8-3693-6381-2.ch012 · Cited by 1
Liu, C., Ma, X., Zhang, H., Xie, S., & Yu, D. (2024) · IEEE Access · doi:10.1109/access.2024.3446281 · Cited by 1
“Topological data analysis (TDA) [35], [36], [37], [38] is applied to EEG signals, and the persistent homology is selected as the neurophysiological features in the brain stimulated by images across the qualities (II).”
MethodInformation Entropy Estimation Based on Point-Set Topology for Hyperspectral Anomaly Detection
Sun, X., Zhuang, L., Gao, L., Gao, H., Sun, X., Liu, Y., Liu, Y., & Zhang, B. (2024) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss53475.2024.10641092 · Cited by 1
“…methods and the difficulties encountered in the costly training process of machine learning-based methods, this paper combines point-set topology with information entropy theories to analyze mathematical-statistical properties of hyperspectral data for detection purposes [19], [20], [21].”
MethodTopological Approach to Void Finding Applied to the SDSS Galaxy Map
Aggarwal, M., Tonegawa, M., Appleby, S. A., Park, C., & Periwal, V. (2024) · The Astrophysical Journal · doi:10.3847/1538-4357/ad75fd · Cited by 1
Tubadji, A., & Rudkin, S. (2024) · Papers of the Regional Science Association · doi:10.1016/j.pirs.2024.100064 · Cited by 1
Hierarchical simplicial manifold learning
Zhang, W., Shih, Y.-H., & Li, J.-S. (2024) · PNAS Nexus · doi:10.1093/pnasnexus/pgae530 · Cited by 1
Topological data analysis in air traffic management: The shape of big flight data sets
Cuerno, M., Guijarro, L., Valdés, R. M. A., & Comendador, F. G. (2025) · PLoS ONE · doi:10.1371/journal.pone.0318108 · Cited by 2
“Topological Data Analysis [18, 20, 54] employs a set of structures, named simplicial complexes, for the purpose of data analysis.”
A cohomology-based Gromov–Hausdorff metric approach for quantifying molecular similarity
Wee, J., Xue, G., Tuschmann, W., & Xia, K. (2025) · Scientific Reports · doi:10.1038/s41598-025-93381-y · Cited by 1
Finding antibodies in cryo-EM maps with CrAI
Mallet, V., Rapisarda, C., Minoux, H., & Ovsjanikov, M. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf157 · Cited by 2
“Once the model makes a prediction, persistence diagrams (Carlsson et al. 2004, Carlsson 2009) are used to select the relevant results which are transformed into a PDB file.”
A statistical framework for analyzing shape in a time series of random geometric objects
van Delft, A. M. N., & Blumberg, A. J. (2025) · The Annals of Statistics · doi:10.1214/24-aos2465 · Cited by 2
Deep Mutual Density Ratio Estimation with Bregman Divergence and Its Applications
Han, D., Zheng, S., Shen, G., Song, X., Sun, L., & Huang, J. (2025) · Journal of the American Statistical Association · doi:10.1080/01621459.2025.2507437 · Cited by 1
“For example, Carlsson (2009) and Pope et al. (2021) showed that image data tend to be supported on an approximate low-dimensional manifold.”
Fluctuation induced network patterns in active matter with spatially correlated noise
Fehlinger, S., Cui, K., Sajjad, A., et al. (2025) · Soft Matter · doi:10.1039/d5sm00245a · Cited by 1
Metadata
- Authors
- Carlsson, G.
- Affiliations
- Stanford University (US)
- Corresponding author
- Carlsson, Gunnar
- ORCID
- Carlsson, Gunnar
- Venue
- Bulletin of the American Mathematical Society
- Publisher
- American Mathematical Society (AMS)
- Published
- 2009-01-29
- Volume
- 46
- Issue
- 2
- Pages
- 255-308
- ISSN
- 0002-9904 (ISSN-L)
- 1088-9485
- 0273-0979
- OpenAlex
- W1991566301
- Cited by
- 2,302
- References
- 78
- Citations by year
- 2026: 215 · 2025: 216 · 2024: 158 · 2023: 176 · 2022: 159 · 2021: 215 · 2020: 227 · 2019: 151 · 2018: 163 · 2017: 132 · 2016: 128 · 2015: 95 · 2014: 99 · 2013: 65 · 2012: 46
- FWCI
- 33.43
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Bulletin of the American Mathematical Society (publishedVersion)
- Funders
- National Science Foundation (0354543)
- Defense Advanced Research Projects Agency
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Topological and Geometric Data Analysis
- Physical Sciences › Mathematics › Geometry and Topology › Geometric and Algebraic Topology
- Physical Sciences › Mathematics › Applied Mathematics › Geometric Analysis and Curvature Flows
- Keywords
- point clouds, distance measures, high-dimensional data, biological data, microarray data, high-throughput data
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