Linear optimal transport embedding: provable Wasserstein classification for certain rigid transformations and perturbations

Caroline Moosmüller, University of North Carolina at Chapel Hill
Alexander Cloninger, University of California, San Diego
Publication Date:
Sep 2022
Project Programs:
Computational Social Science

Discriminating between distributions is an important problem in a number of scientific fields. This motivated the introduction of Linear Optimal Transportation (LOT), which embeds the space of distributions into an L2L2-space. The transform is defined by computing the optimal transport of each distribution to a fixed reference distribution and has a number of benefits when it comes to speed of computation and to determining classification boundaries. In this paper, we characterize a number of settings in which LOT embeds families of distributions into a space in which they are linearly separable. This is true in arbitrary dimension, and for families of distributions generated through perturbations of shifts and scalings of a fixed distribution. We also prove conditions under which the L2L2 distance of the LOT embedding between two distributions in arbitrary dimension is nearly isometric to Wasserstein-2 distance between those distributions. This is of significant computational benefit, as one must only compute NN optimal transport maps to define the N2N2 pairwise distances between NN distributions. We demonstrate the benefits of LOT on a number of distribution classification problems.


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