Approximate Algorithms For $k$-Sparse Wasserstein Barycenter With Outliers

arXiv:2404.13401v1 Announce Type: new
Abstract: Wasserstein Barycenter (WB) is one of the most fundamental optimization problems in optimal transportation. Given a set of distributions, the goal of WB is to find a new distribution that minimizes the average Wasserstein distance to them. The problem becomes even harder if we restrict the solution to be ``$k$-sparse''. In this paper, we study the $k$-sparse WB problem in the presence of outliers, which is a more practical setting since real-world data often contains …

abstract algorithms arxiv cs.lg distribution fundamental optimization outliers set solution them transportation type