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A Wasserstein-type metric for generic mixture models, including location-scatter and group invariant measures

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arxiv 2301.07963 v1 pith:XNMJ4OLQ submitted 2023-01-19 math.OC math.PR

classification math.OCmath.PR
keywords atomsmeasuresmixturesinvariantmetricsomespacewasserstein-type
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In this article, we study Wasserstein-type metrics and corresponding barycenters for mixtures of a chosen subset of probability measures called atoms hereafter. In particular, this works extends what was proposed by Delon and Desolneux [A Wasserstein-Type Distance in the Space of Gaussian Mixture Models. SIAM J. Imaging Sci. 13, 936-970 (2020)] for mixtures of gaussian measures to other mixtures. We first prove in a general setting that for a set of atoms equipped with a metric that defines a geodesic space, the set of mixtures based on this set of atoms is also geodesic space for the defined modified Wasserstein metric. We then focus on two particular cases of sets of atoms: (i) the set of location-scatter atoms and (ii) the set of measures that are invariant with respect to some symmetry group. Both cases are particularly relevant for various applications among which electronic structure calculations. Along the way, we also prove some sparsity and symmetry properties of optimal transport plans between measures that are invariant under some well-chosen symmetries.

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Flowing Datasets with Wasserstein over Wasserstein Gradient Flows

    cs.LG 2025-06 conditional novelty 6.0 of 10

    A new gradient flow framework on the space of probability distributions over probability distributions is introduced and applied to flowing labeled datasets between domains.

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