Persistence-based Modes Inference

Fuente: arXiv
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Autor principal: Henneuse, Hugo
Formato: Preprint
Publicado: 2024
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author Henneuse, Hugo
author_facet Henneuse, Hugo
contents We address the problem of estimating multiple modes of a multivariate density using persistent homology, a central tool in Topological Data Analysis. We introduce a method based on the preliminary estimation of the $H_0$-persistence diagram to infer the number of modes, their locations, and the corresponding local maxima. For broad classes of piecewise-continuous functions with geometric control on discontinuities loci, we identify a critical separation threshold between modes, also interpretable in our framework in terms of modes prominence, below which modes inference is impossible and above which our procedure achieves minimax optimal rates.
format Preprint
id arxiv_https___arxiv_org_abs_2407_15449
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Persistence-based Modes Inference
Henneuse, Hugo
Statistics Theory
Algebraic Topology
We address the problem of estimating multiple modes of a multivariate density using persistent homology, a central tool in Topological Data Analysis. We introduce a method based on the preliminary estimation of the $H_0$-persistence diagram to infer the number of modes, their locations, and the corresponding local maxima. For broad classes of piecewise-continuous functions with geometric control on discontinuities loci, we identify a critical separation threshold between modes, also interpretable in our framework in terms of modes prominence, below which modes inference is impossible and above which our procedure achieves minimax optimal rates.
title Persistence-based Modes Inference
topic Statistics Theory
Algebraic Topology
url https://arxiv.org/abs/2407.15449