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Reconstructing measures on manifolds: an optimal transport approach

Vincent Divol 1, 2
2 DATASHAPE - Understanding the Shape of Data
CRISAM - Inria Sophia Antipolis - Méditerranée , Inria Saclay - Ile de France
Abstract : Assume that we observe i.i.d. points lying close to some unknown d-dimensional C k submanifold M in a possibly high-dimensional space. We study the problem of reconstructing the probability distribution generating the sample. After remarking that this problem is degenerate for a large class of standard losses (L p , Hellinger, total variation, etc.), we focus on the Wasserstein loss, for which we build an estimator, based on kernel density estimation, whose rate of convergence depends on d and the regularity s ≤ k − 1 of the underlying density, but not on the ambient dimension. In particular, we show that the estimator is minimax and matches previous rates in the literature in the case where the manifold M is a d-dimensional cube. The related problem of the estimation of the volume measure of M for the Wasserstein loss is also considered, for which a minimax estimator is exhibited.
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Preprints, Working Papers, ...
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Contributor : Vincent Divol Connect in order to contact the contributor
Submitted on : Monday, February 15, 2021 - 4:22:49 PM
Last modification on : Friday, January 21, 2022 - 3:19:47 AM
Long-term archiving on: : Sunday, May 16, 2021 - 7:51:28 PM


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  • HAL Id : hal-03141977, version 1


Vincent Divol. Reconstructing measures on manifolds: an optimal transport approach. 2021. ⟨hal-03141977⟩



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