Filtration Simplification for Persistent Homology via Edge Contraction

被引:1
|
作者
Dey, Tamal K. [1 ]
Slechta, Ryan [1 ]
机构
[1] Ohio State Univ, Columbus, OH 43210 USA
关键词
Topological data analysis; Persistent homology; Edge contraction; GRAPH MINORS;
D O I
10.1007/s10851-020-00956-7
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Persistent homology is a popular data analysis technique that is used to capture the changing homology of an indexed sequence of simplicial complexes. These changes are summarized in persistence diagrams. A natural problem is to contract edges in complexes in the initial sequence to obtain a sequence of simplified complexes while controlling the perturbation between the original and simplified persistence diagrams. This paper is an extended version of Dey and Slechta (in: Discrete geometry for computer imagery, Springer, New York, 2019), where we developed two contraction operators for the case where the initial sequence is a filtration. In addition to the content in the original version, this paper presents proofs relevant to the filtration case and develops contraction operators for towers and multiparameter filtrations.
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页码:704 / 717
页数:14
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