Linearized optimal transport on manifolds

Fuente: arXiv
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Main Authors: Sarrazin, Clément, Schmitzer, Bernhard
Format: Preprint
Published: 2023
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author Sarrazin, Clément
Schmitzer, Bernhard
author_facet Sarrazin, Clément
Schmitzer, Bernhard
contents Optimal transport is a geometrically intuitive, robust and flexible metric for sample comparison in data analysis and machine learning. Its formal Riemannian structure allows for a local linearization via a tangent space approximation. This in turn leads to a reduction of computational complexity and simplifies combination with other methods that require a linear structure. Recently this approach has been extended to the unbalanced Hellinger--Kantorovich (HK) distance. In this article we further extend the framework in various ways, including measures on manifolds, the spherical HK distance, a study of the consistency of discretization via the barycentric projection, and the continuity properties of the logarithmic map for the HK distance.
format Preprint
id arxiv_https___arxiv_org_abs_2303_13901
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Linearized optimal transport on manifolds
Sarrazin, Clément
Schmitzer, Bernhard
Optimization and Control
Functional Analysis
Optimal transport is a geometrically intuitive, robust and flexible metric for sample comparison in data analysis and machine learning. Its formal Riemannian structure allows for a local linearization via a tangent space approximation. This in turn leads to a reduction of computational complexity and simplifies combination with other methods that require a linear structure. Recently this approach has been extended to the unbalanced Hellinger--Kantorovich (HK) distance. In this article we further extend the framework in various ways, including measures on manifolds, the spherical HK distance, a study of the consistency of discretization via the barycentric projection, and the continuity properties of the logarithmic map for the HK distance.
title Linearized optimal transport on manifolds
topic Optimization and Control
Functional Analysis
url https://arxiv.org/abs/2303.13901