  In this paper we explore domain adaptation through optimal transport. We
propose a novel approach, where we model the data distributions through
Gaussian mixture models. This strategy allows us to solve continuous optimal
transport through an equivalent discrete problem. The optimal transport
solution gives us a matching between source and target domain mixture
components. From this matching, we can map data points between domains, or
transfer the labels from the source domain components towards the target
domain. We experiment with 2 domain adaptation benchmarks in fault diagnosis,
showing that our methods have state-of-the-art performance.
