Cited by 2,331Topological and Geometric Data AnalysisGeometric and Algebraic TopologyGeometric Analysis and Curvature Flows

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 access
Carlsson, Gunnar·2009·Bulletin of the American Mathematical Society

Abstract

An important feature of modern science and engineering is that data of various kinds is being produced at an unprecedented rate. This is so in part because of new experimental methods, and in part because of the increase in the availability of high powered computing technology. It is also clear that the nature of the data we are obtaining is significantly different. For example, it is now often the case that we are given data in the form of very long vectors, where all but a few of the coordinates turn out to be irrelevant to the questions of interest, and further that we don’t necessarily know which coordinates are the interesting ones. A related fact is that the data is often very high-dimensional, which severely restricts our ability to visualize it. The data obtained is also often much noisier than in the past and has more missing information (missing data). This is particularly so in the case of biological data, particularly high throughput data from microarray or other sources. Our ability to analyze this data, both in terms of quantity and the nature of the data, is clearly not keeping pace with the data being produced. In this paper, we will discuss how geometry and topology can be applied to make useful contributions to the analysis of various kinds of data. Geometry and topology are very natural tools to apply in this direction, since geometry can be regarded as the study of distance functions, and what one often works with are distance functions on large finite sets of data. The mathematical formalism which has been developed for incorporating geometric and topological techniques deals with point clouds, i.e. finite sets of points equipped with a distance function. It then adapts tools from the various branches of geometry to the study of point clouds. The point clouds are intended to be thought of as finite samples taken from a geometric object, perhaps with noise. Here are some of the key points which come up when applying these geometric methods to data analysis. • Qualitative information is needed: One important goal of data analysis is to allow the user to obtain knowledge about the data, i.e. to understand how it is organized on a large scale. For example, if we imagine that we are looking at a data set constructed somehow from diabetes patients, it would be important to develop the understanding that there are two types of the disease, namely the juvenile and adult onset forms. Once that is established, one of course wants to develop quantitative methods for distinguishing them, but the first insight about the distinct forms of the disease is key.

Overview

Integrity signals

Registry

Crossref · 10.1090/s0273-0979-09-01249-x

Cites

77 of 78 references verified0 citation errors · 1 not checkable

Statistics

Nothing to recompute.

Verification

Claims the paper makes

53 No basis given · 38 Own interpretation · 23 Own result · 17 No full text of the sources · 9 Supported · 5 Partly supported · 5 Sources not found · 1 Couldn’t confirm · 1 Not checked yet · accuracy not yet measured

Show all 81 claims

  • It may state two separate claims as one. 39
  • Probably not a substantial claim. 41
71 of 71
  • 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.”
10 of 10

Citations

Cites

Show all 78 references

Research works printed but not linked: 4

  1. tigran G. Carlsson and T. Ishkanov, Local structure of spaces of natural images, preprint, (2007), available at http://comptop.stanford.edu/preprints/
  2. Dummit, David S., 2004, Abstract algebra
  3. Huber, Peter J., 1985, Projection pursuit, Ann. Statist., 13, 435, 10.1214/aos/1176349519 · DOI · Paper page
  4. Reeb, Georges, 1946, Sur les points singuliers d’une forme de Pfaff complètement intégrable ou d’une fonction numérique, C. R. Acad. Sci. Paris, 222, 847

Cited by

Sorted by citations

Items 1,401–1,500 of 2,331

Metadata

Authors
Carlsson, G.
Affiliations
Stanford University (US)
Corresponding author
Carlsson, Gunnar
Venue
Bulletin of the American Mathematical Society
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
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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