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Stochastic Wasserstein Barycenters

2020-03-20 | |  46 |   25 |   0

Abstract

We present a stochastic algorithm to compute the barycenter of a set of probability distributions u der the Wasserstein metric from optimal transport. Unlike previous approaches, our method extends to continuous input distributions and allows the support of the barycenter to be adjusted in each iteration. We tackle the problem without regularization, allowing us to recover a much sharper output. We give examples where our algorithm recovers a more meaningful barycenter than previous work. Our method is versatile and can be extended to applications such as generating super samples from a given distribution and recovering blue noise approximations.

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