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.
From: Chris Kapulkin [view email]
[v1]
Tue, 17 Jun 2025 23:12:00 UTC (1,370 KB)
[v2]
Thu, 2 Jul 2026 20:44:05 UTC (8,041 KB)
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