Numeric invariants from multidimensional persistence

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Abstract/Contents

Abstract
Throughout the past decade, there have been many developments and innovations in topological data analysis and persistent homology. The theory of persistent homology is a powerful tool which assigns to any filtered space a persistence module, which is typically interpreted as a module over a ring of polynomials. Although persistent homology serves as a powerful method of analysis for filtered spaces, persistence modules can exhibit a large degree of complexity, especially if the dimension of the filtration is greater than one. Because of the high complexity of persistence modules, they cannot be used directly in the study of concrete data. Instead, one must utilize the structure of persistence modules in order to derive easily understood invariants which provide useful information about the underlying data. This thesis examines the algebraic geometric structure of the space of multidimensional persistence modules and uses the information gleaned to construct numeric invariants. Moreover, these numeric invariants are geometrically meaningful -- if we had calculated these numeric invariants from a filtered space constructed from a (finite) data set, we expect these numeric invariants to encode information about the size, shape, and prominence of the topological features of the data set.

Description

Type of resource text
Form electronic; electronic resource; remote
Extent 1 online resource.
Publication date 2016
Issuance monographic
Language English

Creators/Contributors

Associated with Skryzalin, Jacek
Associated with Stanford University, Department of Mathematics.
Primary advisor Carlsson, Gunnar
Thesis advisor Carlsson, Gunnar
Thesis advisor Cohen, Ralph
Thesis advisor Vakil, Ravi
Advisor Cohen, Ralph
Advisor Vakil, Ravi

Subjects

Genre Theses

Bibliographic information

Statement of responsibility Jacek Skryzalin.
Note Submitted to the Department of Mathematics.
Thesis Thesis (Ph.D.)--Stanford University, 2016.
Location electronic resource

Access conditions

Copyright
© 2016 by Jacek Skryzalin
License
This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).

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