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
Persistent homology; Edge contraction; Topological data analysis;
D O I
10.1007/978-3-030-14085-4_8
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 topology of a filtration associated with some simplicial complex K. These topological changes are summarized in persistence diagrams. We propose two contraction operators which when applied to K and its associated filtration, bound the perturbation in the persistence diagrams. The first assumes that the underlying space of K is a 2-manifold and ensures that simplices are paired with the same simplices in the contracted complex as they are in the original. The second is for arbitrary d-complexes, and bounds the bottleneck distance between the initial and contracted p-dimensional persistence diagrams. This is accomplished by defining interleaving maps between persistence modules which arise from chain maps defined over the filtrations. In addition, we show how the second operator can efficiently compose with itself across multiple contractions. The paper concludes with experiments demonstrating the second operator's utility on manifolds and a brief discussion of future directions for research.
引用
收藏
页码:89 / 100
页数:12
相关论文
共 50 条
  • [11] A Fractal Dimension for Measures via Persistent Homology
    Adams, Henry
    Aminian, Manuchehr
    Farnell, Elin
    Kirby, Michael
    Mirth, Joshua
    Neville, Rachel
    Peterson, Chris
    Shonkwiler, Clayton
    TOPOLOGICAL DATA ANALYSIS, ABEL SYMPOSIUM 2018, 2020, 15 : 1 - 31
  • [12] Distributing Persistent Homology via Spectral Sequences
    Álvaro Torras-Casas
    Discrete & Computational Geometry, 2023, 70 : 580 - 619
  • [13] A Topological Regularizer for Classifiers via Persistent Homology
    Chen, Chao
    Ni, Xiuyan
    Bai, Qinxun
    Wang, Yusu
    22ND INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTICS, VOL 89, 2019, 89
  • [14] Varieties via a filtration of the KBSM and knot contact homology
    Nagasato, Fumikazu
    TOPOLOGY AND ITS APPLICATIONS, 2019, 264 : 251 - 275
  • [15] An analysis of errors in feature-preserving mesh simplification based on edge contraction
    Xu, Hongtao
    Newman, Timothy S.
    THIRD INTERNATIONAL SYMPOSIUM ON 3D DATA PROCESSING, VISUALIZATION, AND TRANSMISSION, PROCEEDINGS, 2007, : 671 - 678
  • [16] Gene Coexpression Network Comparison via Persistent Homology
    Duman, Ali Nabi
    Pirim, Harun
    INTERNATIONAL JOURNAL OF GENOMICS, 2018, 2018
  • [17] Optimal Cycles for Persistent Homology Via Linear Programming
    Escolar, Emerson G.
    Hiraoka, Yasuaki
    OPTIMIZATION IN THE REAL WORLD: TOWARD SOLVING REAL-WORLD OPTIMIZATION PROBLEMS, 2016, 13 : 79 - 96
  • [18] Model comparison via simplicial complexes and persistent homology
    Vittadello, Sean T.
    Stumpf, Michael P. H.
    ROYAL SOCIETY OPEN SCIENCE, 2021, 8 (10):
  • [19] A statistical approach to knot confinement via persistent homology
    Celoria, Daniele
    Mahler, Barbara I.
    PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES, 2022, 478 (2261):
  • [20] Hochschild homology, and a persistent approach via connectivity digraphs
    Caputi L.
    Riihimäki H.
    Journal of Applied and Computational Topology, 2024, 8 (5) : 1121 - 1170