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Topological data analysis using persistent discrete homology

arXiv 2506.15020 ↗First posted 17 Jun 2025math.AT cs.LG math.COcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 33 references that are in the map. First posted on arXiv 17 Jun 2025.

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References · 5

  1. 2022Learnable latent embeddings for joint behavioral and neural analysiscited by 117
  2. 2019Topological Data Analysis for Arrhythmia Detection through Modular Neural Networkscited by 9
  3. 2020Stability of 2-Parameter Persistent Homologycited by 5
  4. 2019Persistent Homotopy Groups of Metric Spacescited by 1
  5. 2024Faster computations of discrete homologycited by 1

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