Stable Vectorization of Multiparameter Persistent Homology using Signed Barcodes as Measures

• URL: http://arxiv.org/abs/2306.03801v2
• Date: Wed, 7 Feb 2024 11:03:48 GMT
• Title: Stable Vectorization of Multiparameter Persistent Homology using Signed Barcodes as Measures
• Authors: David Loiseaux, Luis Scoccola, Mathieu Carri\`ere, Magnus Bakke Botnan, Steve Oudot
• Abstract summary: We show how the interpretation of signed barcodes leads to natural extensions of vectorization strategies. The resulting feature vectors are easy to define and to compute, and provably stable.
• Score: 0.5312303275762102
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Persistent homology (PH) provides topological descriptors for geometric data, such as weighted graphs, which are interpretable, stable to perturbations, and invariant under, e.g., relabeling. Most applications of PH focus on the one-parameter case -- where the descriptors summarize the changes in topology of data as it is filtered by a single quantity of interest -- and there is now a wide array of methods enabling the use of one-parameter PH descriptors in data science, which rely on the stable vectorization of these descriptors as elements of a Hilbert space. Although the multiparameter PH (MPH) of data that is filtered by several quantities of interest encodes much richer information than its one-parameter counterpart, the scarceness of stability results for MPH descriptors has so far limited the available options for the stable vectorization of MPH. In this paper, we aim to bring together the best of both worlds by showing how the interpretation of signed barcodes -- a recent family of MPH descriptors -- as signed measures leads to natural extensions of vectorization strategies from one parameter to multiple parameters. The resulting feature vectors are easy to define and to compute, and provably stable. While, as a proof of concept, we focus on simple choices of signed barcodes and vectorizations, we already see notable performance improvements when comparing our feature vectors to state-of-the-art topology-based methods on various types of data.

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