Gromov-Wassertein-like Distances in the Gaussian Mixture Models Space

arXiv:2310.11256v2 Announce Type: replace-cross
Abstract: The Gromov-Wasserstein (GW) distance is frequently used in machine learning to compare distributions across distinct metric spaces. Despite its utility, it remains computationally intensive, especially for large-scale problems. Recently, a novel Wasserstein distance specifically tailored for Gaussian mixture models and known as MW (mixture Wasserstein) has been introduced by several authors. In scenarios where data exhibit clustering, this approach simplifies to a small-scale discrete optimal transport problem, which complexity depends solely on the number of …

abstract arxiv cs.cv cs.lg machine machine learning novel scale space spaces stat.ml type utility