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.”
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Cited by
Sorted by citations
Items 701–800 of 2,331
Dynamic ECG signal quality evaluation based on persistent homology and GoogLeNet method
Ren, Y., Liu, F., Xia, S., Shi, S., Chen, L., & Wang, Z. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1153386 · Cited by 13
“Topological data analysis method is a data analysis framework based on algebraic topology tools (Carlsson, 2009).”
MethodGuo, W., & Banerjee, A. G. (2016) · IEEE International Symposium on Assembly and Manufacturing · doi:10.1109/isam.2016.7750716 · Cited by 14
“Recently, studies in topological data analysis (TDA) [3]”
BackgroundInfluentialNishio, M., Nishio, M., Nakane, K., et al. (2017) · PLoS ONE · doi:10.1371/journal.pone.0178217 · Cited by 13
“Persistent homology is a method for computing topological features at different spatial resolution [19, 20].”
MethodDiscovery of statistical equivalence classes using computer algebra
Görgen, C., Bigatti, A. M., Riccomagno, E., et al. (2018) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2018.01.003 · Cited by 13
“This graphical representation for a set of monomials has been successfully used in the data analysis of complex systems [5, 9, 22].”
MethodChowdhury, S., Dai, B., & Mémoli, F. (2018) · PLoS ONE · doi:10.1371/journal.pone.0202561 · Cited by 14
“The key principle motivating the following discussion is that the long bars in a barcode correspond to meaningful topological features, whereas short bars correspond to topological noise [10].”
Quantitative Simplification of Filtered Simplicial Complexes
Mémoli, F., & Okutan, O. B. (2019) · Discrete & Computational Geometry · doi:10.1007/s00454-019-00104-y · Cited by 12
“t homology information. 1 Introduction Topological data analysis tries to combine and take advantage of the quantitative (but albeit often noisy) nature of Data and the qualitative nature of Topology [Car09]. This is done through a machinery that assigns a scale dependent family of topological spaces to given dataset…”
Gene Coexpression Network Comparison via Persistent Homology
Duman, A. N., & Pirim, H. (2018) · International Journal of Genomics · doi:10.1155/2018/7329576 · Cited by 13
“Persistent homology is a new tool for studying the shape of a point cloud in application areas such as digital images [9, 10], dynamical systems [11], biomolecules [12], and highdimensional data mining [13].”
MethodBaudot, P. (2019) · Entropy · doi:10.3390/e21090881 · Cited by 12
“On the side of applied algebraic topology, the identification of the topological structures of a dataset has motivated important research following the development of persistent homology [53–55].”
Background(Hyper)graph Kernels over Simplicial Complexes
Martino, A., & Rizzi, A. (2020) · Entropy · doi:10.3390/e22101155 · Cited by 17
Multidimensional persistent homology is stable
Cerri, A., Fabio, B. D., Ferri, M., Frosini, P., & Landi, C. (2009) · AMS Acta (University of Bologna) · doi:10.6092/unibo/amsacta/2603 · Cited by 14
Jamshidi, A. A., Kirby, M. J. L., & Broomhead, D. (2011) · IEEE Signal Processing Magazine · doi:10.1109/msp.2010.939550 · Cited by 13
Arsuaga, J., Baas, N. A., DeWoskin, D., Mizuno, H., Pankov, A., & Park, C. (2012) · Applicable Algebra in Engineering Communication and Computing · doi:10.1007/s00200-012-0166-8 · Cited by 13
“One approach is to use computational algebraic topology and/or persistent homology (reviewed in [6,14,39,9,17]).”
MethodComparative Topological Signatures of Growing Collaboration Networks
Pal, S., Moore, T. J., Ramanathan, R., & Swami, A. (2017) · Springer proceedings in complexity · doi:10.1007/978-3-319-54241-6_18 · Cited by 13
Topological De-Noising: Strengthening the Topological SignalPreprint
Kloke, J., & Carlsson, G. (2009) · arXiv (Cornell University) · doi:10.48550/arxiv.0910.5947 · Cited by 14
“To compute homology from point cloud data D , the algorithm of persistent homology builds a sequence of nested simplicial complexes with the points in D being the vertices of the complexes (see [3] for more details).”
Self-citationThe Topological 'Shape' of BrexitPreprint
Stolz, B. J., Harrington, H. A., & Porter, M. A. (2016) · SSRN Electronic Journal · doi:10.2139/ssrn.2843662 · Cited by 20
“; referendum in the United Kingdom. These examples consider a topical situation and give useful illustrations of the strengths and weaknesses of these methods. 1 Introduction Persistent homology (PH) [5, 8{11, 16] is a method from computational algebraic topology that can be used to study the \shape" of data. The…”
BackgroundInterleaving and Gromov-Hausdorff distancePreprint
Bubenik, P., de Silva, V., & Scott, J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.06288 · Cited by 13
“Introduction Persistent homology [26, 46] has been a highly successful tool in applied topology [27, 15].”
MethodPersistent Homology for The Quantitative Prediction of Fullerene StabilityPreprint
Xia, K., Feng, X., Tong, Y., et al. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1412.2369 · Cited by 13
Topological Detection of Alzheimer’s Disease Using Betti Curves
Saadat-Yazdi, A., Andreeva, R., & Sarkar, R. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87444-5_12 · Cited by 12
“Topo-logical data analysis (TDA) is made robust through the use of topological persistence [31,6] – working off the assumption that structures which persist over multiple scales of data, or multiple values of a relevant filtration function , are important.”
BackgroundMethodKiyomura, K., Wang, Z., Ogawa, T., et al. (2021) · ISIJ International · doi:10.2355/isijinternational.isijint-2021-197 · Cited by 12
Uncertainty, volatility and the persistence norms of financial time series
Rudkin, S., Qiu, W., & Dłotko, P. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.119894 · Cited by 12
“TDA is introduced in the seminal work of Carlsson (2009), and is widely developed for, the natural sciences (Bubenik and D lotko, 2017; Bubenik, 2020).”
BackgroundRisk analysis of China’s stock markets based on topological data structures
Guo, H., Yu, H., An, Q., & Zhang, X. (2022) · Procedia Computer Science · doi:10.1016/j.procs.2022.04.028 · Cited by 12
Arfi, B. (2023) · Quality & Quantity · doi:10.1007/s11135-023-01708-6 · Cited by 14
“I focus in this introduction on the Vietoris-Rips complex because it is the simplest and is efficient enough in computing topological features (Carlsson 2009; Zomorodian 2009; Ghrist 2014) (see the Appendix).”
Aglikov, A. S., Жуков, М. В., Aliev, T. A., Kozodaev, D. A., Nosonovsky, M., & Скорб, Е. В. (2024) · Applied Surface Science · doi:10.1016/j.apsusc.2024.160640 · Cited by 12
Identifying extreme events in the stock market: A topological data analysis
Rai, A., Sharma, B. N., Luwang, S. R., et al. (2024) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0220424 · Cited by 12
Carlsson, G. (2016) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2016.12.012 · Cited by 12
“For a more detailed description of the method, see [3,4].”
MethodSelf-citationTopological data analysis (TDA) applied to reveal pedogenetic principles of European topsoil system
Savic, A. G., Tóth, G., & Duponchel, L. (2017) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2017.02.095 · Cited by 12
Closed trail distance in a biconnected graph
Snåšel, V., Dráždilová, P., & Platoš, J. (2018) · PLoS ONE · doi:10.1371/journal.pone.0202181 · Cited by 12
Hyppo-X: A Scalable Exploratory Framework for Analyzing Complex Phenomics Data
Kamruzzaman, M., Kalyanaraman, A., Krishnamoorthy, B., Hey, S., & Schnable, P. S. (2019) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2019.2947500 · Cited by 12
“Our approach uses emerging principles from algebraic topology [5], [6] as the basis to observe and discern structural features from raw phenomics data.”
MethodFast Computation of Persistent Homology with Data Reduction and Data Partitioning
Malott, N. O., & Wilsey, P. A. (2019) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata47090.2019.9006572 · Cited by 13
“GUDHI is a large C++ library for topological data analysis supporting many options of (i) complex types, (ii) complex reduction algorithms, and (iii) data analysis to support a larger understanding of TDA.”
BackgroundInfluentialTowards computational analytics of 3D neuron images using deep adversarial learning
Li, Z., Fan, X., Shang, Z., Zhang, L., Zhen, H., & Fang, C. (2021) · Neurocomputing · doi:10.1016/j.neucom.2020.03.129 · Cited by 12
Embedding Functional Brain Networks in Low Dimensional Spaces Using Manifold Learning Techniques
Casanova, R., Lyday, R. G., Bahrami, M., Burdette, J. H., Simpson, S. L., & Laurienti, P. J. (2021) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2021.740143 · Cited by 12
“Saggar et al., used topological data analysis (Carlsson, 2009) combined with t-SNE to reveal the overall organization of whole-brain activity maps at a single-participant level without arbitrarily collapsing the data (Saggar et al., 2018).”
MethodPersistent homology of Z2 gauge theories
Sehayek, D., & Melko, R. G. (2022) · Physical review. B./Physical review. B · doi:10.1103/physrevb.106.085111 · Cited by 19
Dey, A. K., Young, S. J., & Gel, Y. R. (2023) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1093/jrsssa/qnad066 · Cited by 12
Manifold Learning in Regression Tasks
Bernstein, A., Kuleshov, A., & Yanovich, Y. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-17091-6_36 · Cited by 12
A New Approximation Algorithm for the Matching Distance in Multidimensional Persistence
Cerri, A., & Frosini, P. (2020) · Journal of Computational Mathematics · doi:10.4208/jcm.1809-m2018-0043 · Cited by 11
ClassiMap: A New Dimension Reduction Technique for Exploratory Data Analysis of Labeled Data
Lespinats, S., Aupetit, M. J. M., & Meyer‐Baese, A. (2015) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001415510088 · Cited by 12
“Exploratory Data Analysis (EDA) [69] aims at extracting descriptive information about the data at hand that can be used by humans for knowledge discovery: correlation, clustering, outlier detection, intrinsic dimension estimation, Principal Component Analysis (see [81] for a general overview) or more recently Topologi…”
BackgroundTowards topological analysis of high-dimensional feature spaces
Wagner, H., & Dłotko, P. (2014) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2014.01.005 · Cited by 12
“For example, [2] shows that data coming from natural images form a topological Klein bottle.”
BackgroundCoverage statistics for sequence census methods
Evans, S. N., Hower, V., & Pachter, L. S. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-430 · Cited by 11
“Our approach is motivated by recent applications of topology including persistent homology [12,13] and the use of critical points in shape analysis [14-16].”
MethodModelling persistence diagrams with planar point processes, and revealing topology with bagplots
Adler, R. J., & Agami, S. (2019) · Journal of Applied and Computational Topology · doi:10.1007/s41468-019-00035-w · Cited by 12
“For the reader wanting a more general approach, there are the recent excellent and quite different books and reviews Carlsson (2009), Carlsson (2014), Edelsbrunner (2014), Edelsbrunner and Harer (2008), Edelsbrunner and Harer (2010), Zomorodian (2005), Oudot (2015) and Ghrist (2014), and, in…”
BackgroundTuning the performance of a computational persistent homology package
Hylton, A., Henselman‐Petrusek, G., Sang, J., & Short, R. (2019) · Software Practice and Experience · doi:10.1002/spe.2678 · Cited by 12
“Excellent surveys may be found in a number of sources, such as (8, 9, 10, 11).”
BackgroundMETRIC THICKENINGS, BORSUK–ULAM THEOREMS, AND ORBITOPES
Adams, H., Bush, J., & Frick, F. (2019) · Mathematika · doi:10.1112/mtk.12010 · Cited by 12
“Recently, Vietoris–Rips thickenings have been used in topological data analysis and persistent homology [15, 16]; specifically, they allow for a growing filtration of topological spaces associated to a finite collection or sampling of data.”
MethodOn homotopy types of Vietoris–Rips complexes of metric gluings
Adamaszek, M., Adams, H., Gasparovic, E., Gommel, M., Purvine, E., Sazdanović, R., Wang, B., Wang, Y., & Ziegelmeier, L. (2020) · Journal of Applied and Computational Topology · doi:10.1007/s41468-020-00054-y · Cited by 19
“A graph G is a metric graph if each edge e j is assigned a positive finite length l j (Bridson and Haefliger 1999; Burago et al. 2001; Kuchment 2004).”
BackgroundTwisty Takens: a geometric characterization of good observations on dense trajectories
Xu, B., Tralie, C. J., Antia, A., et al. (2019) · Journal of Applied and Computational Topology · doi:10.1007/s41468-019-00036-9 · Cited by 12
Clustering Activity–Travel Behavior Time Series using Topological Data Analysis
Chen, R., Zhang, J., Равишанкер, Н., et al. (2019) · Journal of Big Data Analytics in Transportation · doi:10.1007/s42421-019-00008-6 · Cited by 12
“Using computational homology, TDA is aimed at analyzing the topological features of data and representing these features using low-dimensional representations (Carlsson 2009).”
MethodTopological Data Analysis of Vascular Disease: A Theoretical Framework
Nicponski, J., & Jung, J.-H. (2020) · Frontiers in Applied Mathematics and Statistics · doi:10.3389/fams.2020.00034 · Cited by 12
“The primary topological feature we explore is that of persistent homology [1].”
BackgroundInfluentialGeometric Approaches to Persistent Homology
Adams, H., & Coşkunuzer, B. (2022) · SIAM Journal on Applied Algebra and Geometry · doi:10.1137/21m1422914 · Cited by 14
“In the past decade, topological data analysis (TDA) has grown substantially, and has proven to be quite useful to understand many phenomena described via different types of data [16].”
BackgroundCochain level May–Steenrod operations
Kaufmann, R. M., & Medina-Mardones, A. M. (2021) · Forum Mathematicum · doi:10.1515/forum-2020-0296 · Cited by 12
A Survey on the High Performance Computation of Persistent Homology
Malott, N. O., Chen, S., & Wilsey, P. A. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3147070 · Cited by 12
“Each of these studies, along with several other surveys [38], [39], [40], effectively describe the background concepts of persistent homology.”
BackgroundResultManin, Y. I., & Marcolli, M. (2016) · Mathematics in Computer Science · doi:10.1007/s11786-016-0278-9 · Cited by 12
“In this setting, we will show that the resulting grouping can be studied in terms of persistent topology [7].”
BackgroundGeometry of spiking patterns in early visual cortex: a topological data analytic approach
Guidolin, A., Desroches, M., Victor, J. D., Purpura, K. P., & Rodrigues, S. (2022) · Journal of The Royal Society Interface · doi:10.1098/rsif.2022.0677 · Cited by 11
Structure of the Afferent Terminals in Terminal Ganglion of a Cricket and Persistent Homology
Brown, J. D., & Gedeon, T. (2012) · PLoS ONE · doi:10.1371/journal.pone.0037278 · Cited by 12
“It uses ideas from persistent homology [1-3].”
Persistent Homology for Learning Densities with Bounded Support
Pokorny, F. T., Ek, C. H., Hedvig, E., & Kragic, K. D. (2012) · Cited by 11
Towards an Observer-oriented Theory of Shape Comparison
Frosini, P. (2016) · CINECA IRIS Institutial research information system (University of Pisa) · doi:10.2312/3dor.20161080 · Cited by 12
“In this position paper we intend to consider this issue and propose a possible solution, framing it in the emerging field of Topological Data Analysis [Car09].”
MethodStabilizing the unstable output of persistent homology computations
Bendich, P. L., Bubenik, P., & Wagner, A. (2019) · Journal of Applied and Computational Topology · doi:10.1007/s41468-019-00044-9 · Cited by 11
Rapoport, D. L. (2013) · Journal of Physics Conference Series · doi:10.1088/1742-6596/437/1/012024 · Cited by 14
Quantum algorithms for topological and geometric analysis of big dataPreprint
Lloyd, S., Garnerone, S., & Zanardi, P. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1408.3106 · Cited by 12
“Topological techniques for analyzing big data represent a sophisticated and powerful tool [1-9]: by its very nature, topology reveals features of the data that do not depend on”
MethodManifold learning for fMRI time-varying functional connectivity
González-Castillo, J., Fernandez, I. S., Lam, K. C., Handwerker, D. A., Pereira, F., & Bandettini, P. A. (2023) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2023.1134012 · Cited by 11
“Yet, it is worth noting that UMAP builds on strong mathematical foundations from the field of topological data analysis (TDA; Carlsson, 2009), and that, it is those foundations [as described in Section 2 of McInnes et al.”
BackgroundTopological data analysis assisted automated sleep stage scoring using airflow signals
Chung, Y.-M., Huang, W. K., & Wu, H. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105760 · Cited by 11
“Topological data analysis (TDA), a modern data analysis framework motivated by analyzing complicated high dimensional datasets, has gained a significant amount of attention as a versatile tool for data exploration and analysis [15, 27, 16].”
BackgroundImperial College near infrared spectroscopy neuroimaging analysis framework
Orihuela‐Espina, F., Leff, D. R., James, D. R. C., Darzi, A. W., & Yang, G. (2017) · Neurophotonics · doi:10.1117/1.nph.5.1.011011 · Cited by 12
“Second, optimizing the choice of the neighborhood size defining the solution using persistent homotopies.(48) Third, by exploiting causal Lorentzian manifolds to express effective connectivity.”
MethodInferring Temporal Phenotypes with Topological Data Analysis and Pseudo Time-Series
Dagliati, A., Geifman, N., Peek, N. B., Holmes, J. H., Sacchi, L., Sajjadi, S. E., & Tucker, A. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-21642-9_50 · Cited by 11
Scale-variant topological information for characterizing the structure of complex networks
Tran, Q. H., Vo, V. T., & Hasegawa, Y. (2019) · Physical review. E · doi:10.1103/physreve.100.032308 · Cited by 11
“To obtain a robust representation, we use the topological data analysis 26–29 , which is a set of computational topology methods, to encode the simplicial complex model into noise-tolerant quantitative features.”
MethodGrain analysis of atomic force microscopy images via persistent homology
Duman, A. N. (2020) · Ultramicroscopy · doi:10.1016/j.ultramic.2020.113176 · Cited by 11
On Vietoris–Rips complexes of hypercube graphs
Adamaszek, M., & Adams, H. (2021) · Journal of Applied and Computational Topology · doi:10.1007/s41468-021-00083-1 · Cited by 11
“Indeed, one of the most frequently computed forms of persistent homology is obtained by building a Vietoris–Rips simplicial complex filtration on top of a potentially high-dimensional dataset [17, 13].”
BackgroundLeave-One-Out Kernel Density Estimates for Outlier Detection
Kandanaarachchi, S., & Hyndman, R. J. (2021) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2021.2000425 · Cited by 11
“Persistent homology has been used in a wide variety of applications including biology (Topaz, Ziegelmeier & Halverson 2015), computer graphics (Carlsson et al. 2008) and engineering (Perea & Harer 2015). In this section we will give a brief overview of persistent homology without delving into the mathematical details.…”
BackgroundMethodIdentifying porous cage subsets in the Cambridge Structural Database using topological data analysis
Li, A., Bueno-Pérez, R., & Fairen‐Jiménez, D. (2022) · Chemical Science · doi:10.1039/d2sc03171j · Cited by 12
Dynamics of tissue topology during cancer invasion and metastasis
Munn, L. L. (2013) · Physical Biology · doi:10.1088/1478-3975/10/6/065003 · Cited by 11
“The Euler number, designated χ , has a characteristic value for each topological configuration and is affected by the number of objects, holes and cavities as shown in figure 1 [3].”
BackgroundAndreev, V. P., & Gillespie, B. W. (2016) · Journal of Proteomics & Bioinformatics · doi:10.4172/jpb.s14-005 · Cited by 10
“Recently, this group of methods was complemented by an even more sensitive classification technique called topological data analysis [5], which proved to be useful in a broad range of multidimensional data analysis applications ranging from detection of subtypes of breast cancer [6] to exploring the states of folding…”
Maletić, S., & Zhao, Y. (2017) · Entropy · doi:10.3390/e19040172 · Cited by 12
Quantification of LSS using the persistent homology in the SDSS fields
Kimura, Y., & Imai, K. (2017) · Advances in Space Research · doi:10.1016/j.asr.2017.04.020 · Cited by 11
HodgeRank With Information Maximization for Crowdsourced Pairwise Ranking Aggregation
Xu, Q., Xiong, J., Chen, X., et al. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11619 · Cited by 11
“Given a stream of paired comparisons, persistent homology (Edelsbrunner, Letscher, and Zomorodian 2002; Carlsson 2009) is in fact an online algorithm to check topology evolution when simplices (e.”
BackgroundTopological data analysis and diagnostics of compressible magnetohydrodynamic turbulence
Makarenko, I., Bushby, P., Fletcher, A., et al. (2018) · Journal of Plasma Physics · doi:10.1017/s0022377818000752 · Cited by 11
“Over the last decade, topological approaches have been widely applied to the analysis of random fields (Carlsson 2009; Adler et al. 2010; Adler & Taylor 2011; Edelsbrunner 2014; Yogeshwaran & Adler 2015).”
BackgroundUse of Topological Data Analysis in Motor Intention Based Brain-Computer Interfaces
Altindış, F., Yılmaz, B., Borisenok, S., & İçöz, K. (2018) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco.2018.8553382 · Cited by 12
“TDA interpretations use different homology properties such as connectivity, surface, edge and volume [19].”
BackgroundZhang, S., Xiao, M., Guo, C., Geng, L., Wang, H., & Zhang, X. (2019) · International Conference on Supercomputing · doi:10.1145/3330345.3332147 · Cited by 11
“It is important to find and understand the shape of data in multiple dimensions, which is a major research theme of Topological Data Analysis (TDA) [10].”
BackgroundRobust Satisfiability of Systems of Equations
Franek, P., & Krčál, M. (2013) · ACM-SIAM Symposium on Discrete Algorithms (SODA) · doi:10.1137/1.9781611973402.14 · Cited by 11
Impact-induced hardening in dense frictional suspensions
Pradipto, & Hayakawa, H. (2021) · Physical Review Fluids · doi:10.1103/physrevfluids.6.033301 · Cited by 11
“To elucidate the role of the force chains in impact-induced hardening, we analyze the topological structure of force chains by using persistent homology analysis [37].”
MethodSimilarity-Based Prediction for Channel Mapping and User Positioning
Magoarou, L. L. (2021) · IEEE Communications Letters · doi:10.1109/lcomm.2021.3049849 · Cited by 11
“Actually, 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 [23], [24] [7, Section 5.”
MethodTopological Data Analysis for Eye Fundus Image Quality Assessment
Avilés-Rodríguez, G. J., Hipólito, J. I. N., Cosío-León, M. D. L. Á., et al. (2021) · Diagnostics · doi:10.3390/diagnostics11081322 · Cited by 11
“Techniques such as image structure clustering [22] or segmentation maps and feature analysis fall on this category [23]. obtention of homological groups (Betti numbers, β { 1,2 } ) to be used in the process selecting optimal threshold values”
BackgroundMethodInfluentialUtilising urgent computing to tackle the spread of mosquito-borne diseases
Brown, N., Nash, R. W., Poletti, P., et al. (2021) · doi:10.1109/urgenthpc54802.2021.00010 · Cited by 11
“Topological Data Analysis (TDA) [11] is one such solution, and this captures the features of interest in scalar data into concise, yet informative, topological data signatures.”
BackgroundComputing persistent features in big data: A distributed dimension reduction approach
Wilkerson, A. C., Chintakunta, H., & Krim, H. (2014) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2014.6853548 · Cited by 11
“[3] One popular visualization is the barcode, a chart whose x-axis is the range of E, and whose y-axis is discrete , having one entry for each persistent homology class.”
BackgroundMethodExtended Topological Persistence and Contact Arrangements in Folded Linear Molecules
Kališnik, S., & Mashaghi, A. (2016) · Frontiers in Applied Mathematics and Statistics · doi:10.3389/fams.2016.00006 · Cited by 11
“We present the theoretical material in a compact, informal manner following [4, 5] and encourage the readers interested in details to read the given references.”
BackgroundInfluentialDetection of low dimensionality and data denoising via set estimation techniques
Aaron, C., Cholaquidis, A., & Cuevas, A. (2017) · Electronic Journal of Statistics · doi:10.1214/17-ejs1370 · Cited by 11
Állili, M., Kaczyński, T., Landi, C., & Masoni, F. (2018) · Journal of Mathematical Imaging and Vision · doi:10.1007/s10851-018-0843-8 · Cited by 11
On the asymptotic normality of persistent Betti numbers
Krebs, J., & Polonik, W. (2024) · Advances in Applied Probability · doi:10.1017/apr.2024.61 · Cited by 11
“(2000), Zomorodian and Carlsson (2005) and Carlsson (2009).”
A Notion of Harmonic Clustering in Simplicial Complexes
Ebli, S., & Spreemann, G. (2019) · IEEE International Conference on Machine Learning and Applications (ICMLA) · doi:10.1109/icmla.2019.00182 · Cited by 11
“topological data analysis (TDA) as a field [8], [9] (see also [10]– [13] for modern introductions and surveys), have confirmed the usefulness of viewing data through a higher-dimensional analog of graphs [14], [15].”
BackgroundUsing extremal events to characterize noisy time series
Berry, E., Cummins, B., Nerem, R. R., et al. (2020) · Journal of Mathematical Biology · doi:10.1007/s00285-020-01471-4 · Cited by 11
Alfaleh, A. A., Wang, Y., Yan, B., et al. (2015) · SPE Annual Technical Conference and Exhibition · doi:10.2118/174985-ms · Cited by 11
Persistence Images: An Alternative Persistent Homology Representation.Preprint
Chepushtanova, S., Emerson, T., Hanson, E., Kirby, M. J. L., Motta, F. C., Neville, R., Peterson, C., Shipman, P. D., & Ziegelmeier, L. (2015) · arXiv (Cornell University) · Cited by 11
Bazaikin, Y., Байков, В. А., Taimanov, I. A., & Yakovlev, A. A. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1302.6885 · Cited by 10
“A detailed account of persistent homology and applications can be found in [8, 9, 10, 11, 12, 13, 14, 15, 16].”
BackgroundABSTRACT AND CLASSICAL HODGE–DE RHAM THEORY
Smale, N., & Smale, S. (2012) · Analysis and Applications · doi:10.1142/s0219530512500054 · Cited by 11
Persistence modules: Algebra and algorithmsPreprint
Škraba, P., & Vejdemo‐Johansson, M. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1302.2015 · Cited by 13
“For surveys or books on the subject, we recommend: [6, 18, 23, 36].”
BackgroundRandomly Weighted $d-$complexes: Minimal Spanning Acycles and Persistence DiagramsPreprint
Škraba, P., Thoppe, G., & Yogeshwaran, D. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1701.00239 · Cited by 11
“ersistent homology provides a formal tool to understand how the topology of a ltration evolves as the ltration parameter changes. For more complete introduction and survey of persistent homology, see [25, 9, 10]. We now describe the natural ltration associated with weighted simplicial complexes. Consider a simplicial…”
BackgroundKnowledge gaps in the early growth of semantic networksPreprint
Sizemore, A. E., Karuza, E. A., Giusti, C., & Bassett, D. S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1709.00133 · Cited by 9
“We can then compute persistent homology [11, 62], which tracks the formation and possible filling in of topological cavities of different dimensions throughout a filtration.”
BackgroundMethodInfluentialGeneralized Persistence Algorithm for Decomposing Multi-parameter Persistence ModulesPreprint
Dey, T. K., & Xin, C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.03766 · Cited by 11
“The persistence algorithm [29] and its algebraic formulation afterward [52] seeded the area called topological data analysis (TDA) that is addressing various problems in data science [10, 13, 22, 31, 43, 44, 47, 48, 49, 51].”
BackgroundA numerical measure of the instability of Mapper-type algorithmsPreprint
Belchí, F., Brodzki, J., Burfitt, M., & Niranjan, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.01507 · Cited by 11
“The success of topological data analysis rests on the discovery, demonstrated in many groundbreaking results, that methods from algebraic topology can provide insight into the structure and meaning of complex, multidimensional data [13].”
BackgroundUrban Morphology Meets Deep Learning
Moosavi, V. (2022) · doi:10.1002/9781119815075.ch29 · Cited by 11
Alagappan, M., Jiang, D., Denko, N., & Koong, A. C. (2016) · Advances in Experimental Medicine and Biology · doi:10.1007/978-3-319-26666-4_15 · Cited by 10
A topological data analysis based classifier
Kindelan, R., Frías, J., Cerda, M., et al. (2023) · Advances in Data Analysis and Classification · doi:10.1007/s11634-023-00548-4 · Cited by 10
“successful in reducing dimensionality, and it has demonstrated robustness to noise by inferring the underlying dataset’s topology [1, 2].”
BackgroundDetecting Carbon Nanotube Orientation with Topological Analysis of Scanning Electron Micrographs
Dong, L., Hang, H., Park, J. G., Mio, W., & Liang, R. (2022) · Nanomaterials · doi:10.3390/nano12081251 · Cited by 10
“blooming of TDA, which offers solutions to complex image and data analysis problems in diverse interdisciplinary applications [17-19].”
BackgroundMoon, C., Li, Q., & Xiao, G. (2023) · The Annals of Applied Statistics · doi:10.1214/22-aoas1714 · Cited by 11
“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).”
MethodWilliamson, S. M., & Prybutok, V. R. (2024) · International Journal of Information Management Data Insights · doi:10.1016/j.jjimei.2024.100278 · Cited by 10
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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