CO-Optimal Transport

• URL: http://arxiv.org/abs/2002.03731v3
• Date: Fri, 6 Nov 2020 14:31:21 GMT
• Title: CO-Optimal Transport
• Authors: Ievgen Redko, Titouan Vayer, R\'emi Flamary, Nicolas Courty
• Abstract summary: Optimal transport (OT) is a powerful tool for finding correspondences and measuring similarity between two distributions. We propose a novel OT problem, named COOT for CO- Optimal Transport, that simultaneously optimize two transport maps between both samples and features. We demonstrate its versatility with two machine learning applications in heterogeneous domain adaptation and co-clustering/data summarization.
• Score: 19.267807479856575
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Optimal transport (OT) is a powerful geometric and probabilistic tool for finding correspondences and measuring similarity between two distributions. Yet, its original formulation relies on the existence of a cost function between the samples of the two distributions, which makes it impractical when they are supported on different spaces. To circumvent this limitation, we propose a novel OT problem, named COOT for CO-Optimal Transport, that simultaneously optimizes two transport maps between both samples and features, contrary to other approaches that either discard the individual features by focusing on pairwise distances between samples or need to model explicitly the relations between them. We provide a thorough theoretical analysis of our problem, establish its rich connections with other OT-based distances and demonstrate its versatility with two machine learning applications in heterogeneous domain adaptation and co-clustering/data summarization, where COOT leads to performance improvements over the state-of-the-art methods.

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