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Understanding the Topology and the Geometry of the Space of Persistence Diagrams via Optimal Partial Transport

Vincent Divol 1 Théo Lacombe 1
1 DATASHAPE - Understanding the Shape of Data
CRISAM - Inria Sophia Antipolis - Méditerranée , Inria Saclay - Ile de France
Abstract : Despite the obvious similarities between the metrics used in topological data analysis and those of optimal transport, an optimal-transport based formalism to study persistence diagrams and similar topological descriptors has yet to come. In this article, by considering the space of persistence diagrams as a space of discrete measures, and by observing that its metrics can be expressed as optimal partial transport problems, we introduce a generalization of persistence diagrams, namely Radon measures supported on the upper half plane. Such measures naturally appear in topological data analysis when considering continuous representations of persistence diagrams (e.g.\ persistence surfaces) but also as limits for laws of large numbers on persistence diagrams or as expectations of probability distributions on the persistence diagrams space. We explore topological properties of this new space, which will also hold for the closed subspace of persistence diagrams. New results include a characterization of convergence with respect to Wasserstein metrics, a geometric description of barycenters (Fr\'echet means) for any distribution of diagrams, and an exhaustive description of continuous linear representations of persistence diagrams. We also showcase the strength of this framework to study random persistence diagrams by providing several statistical results made meaningful thanks to this new formalism.
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https://hal.inria.fr/hal-01970466
Contributor : Théo Lacombe <>
Submitted on : Friday, July 17, 2020 - 10:57:07 AM
Last modification on : Monday, February 15, 2021 - 1:45:10 PM

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Vincent Divol, Théo Lacombe. Understanding the Topology and the Geometry of the Space of Persistence Diagrams via Optimal Partial Transport. Journal of Applied and Computational Topology, Springer, 2020, ⟨10.1007/s41468-020-00061-z⟩. ⟨hal-01970466v3⟩

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