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arXiv · 2506.15020

Topological data analysis using persistent discrete homology

Chris Kapulkin·Nathan Kershaw
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Abstract

We propose persistent discrete homology as a tool for topological data analysis and discuss its advantages over the existing methods. In particular, we provide empirical evidence that persistent discrete homology is more noise-resistant than persistent homology of the Vietoris-Rips complex for data coming from non-metric settings.

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Chris Kapulkin, Nathan Kershaw. 2025-06-17. Topological data analysis using persistent discrete homology. https://arxiv.org/abs/2506.15020

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