Collaborative Clustering Through Optimal Transport

Abstract

Significant results have been achieved recently by exchanging information between multiple learners for clustering tasks. However, this approaches still suffer from a few issues regarding the choice of the information to trade, the stopping criteria and the trade-of between the information extracted from the data and the information exchanged by the models. We aim in this paper to address this issues through a novel approach propelled by the optimal transport theory. More specifically, the objective function is based on the Wasserstein metric, with a bidirectional transport of the information. This formulation leads to a high stability and increase of the quality. It also allows the learning of a stopping criteria. Extensive experiments were conducted on multiple data sets to evaluate the proposed method, which confirm the advantages of this approach.