mlsanityData to 7 Oct 2026

An Introduction to Multiparameter Persistence

arXiv 2203.14289 ↗First posted 27 Mar 2022math.AT cs.CG math.RTcited by 15

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Abstract (arXiv)

In topological data analysis (TDA), one often studies the shape of data by constructing a filtered topological space, whose structure is then examined using persistent homology. However, a single filtered space often does not adequately capture the structure of interest in the data, and one is led to consider multiparameter persistence, which associates to the data a space equipped with a multiparameter filtration. Multiparameter persistence has become one of the most active areas of research within TDA, with exciting progress on several fronts. In this article, we introduce multiparameter persistence and survey some of this recent progress, with a focus on ideas likely to lead to practical applications in the near future.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Mar 2022.

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Citing papers per year: 2022: 3; 2023: 5; 2024: 2; 2025: 3; 2026: 2.

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