Persistent extensions and analogous bars: data-induced relations between persistence barcodes

Journal article

Abstract A central challenge in topological data analysis is the interpretation of barcodes. The classical algebraic-topological approach to interpreting homology classes is to build maps to spaces whose homology carries semantics we understand and then to appeal to functoriality. However, we often lack such maps in real data; instead, we must rely on a cross-dissimilarity measure between our observations of a system and a reference. In this paper, we develop a pair of computational homological algebra approaches for relating persistent homology classes and barcodes: persistent extension , which enumerates potential relations between homology classes from two complexes built on the same vertex set, and the method of analogous bars , which utilizes persistent extension and the witness complex built from a cross-dissimilarity measure to provide relations across systems. We provide an implementation of these methods and demonstrate their use in comparing homology classes between two samples from the same metric space and determining whether topology is maintained or destroyed under clustering and dimensionality reduction.

Authors: Yoon, HR; Ghrist, R; Giusti, C

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Springer
• Journal: Journal of Applied and Computational Topology
• Volume: 7
• Issue: 3
• Pages: 571-617
• Publication date: 2023-04-15
• DOI: 10.1007/s41468-023-00115-y
• EISSN: 2367-1734
• ISSN: 2367-1726
• Language: English
• Keywords: Mathematics; Topological data analysis; Homology (biology); Singular homology; Topology (electrical circuits); Computer science; Algorithm; Theoretical computer science; Persistent homology; Combinatorics; Algebraic number; Artificial intelligence; Cluster analysis