Gromov-Wasserstein Methods for Multi-View Relational Embedding and Clustering

• URL: http://arxiv.org/abs/2604.23912v1
• Date: Sun, 26 Apr 2026 23:23:12 GMT
• Title: Gromov-Wasserstein Methods for Multi-View Relational Embedding and Clustering
• Abstract summary: We propose Bary-GWMDS, a Gromov-Wasserstein-based method that operates directly on distance matrices to learn a consensus embedding.<n>We also introduce Mean-GWMDS-C, a clustering-oriented formulation that averages distance matrices and learns reduced-support representations via a consensus Gromov-Wasserstein transport.<n>Experiments on synthetic and real-world datasets show that the proposed framework yields stable and geometrically meaningful embeddings.
• Score: 7.216399430290167
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning low-dimensional representations from multi-view relational data is challenging when underlying geometries differ across views. We propose Bary-GWMDS, a Gromov-Wasserstein-based method that operates directly on distance matrices to learn a consensus embedding preserving shared relational structure. By leveraging intrinsic distances, the approach naturally handles nonlinear distortions across views. We also introduce Mean-GWMDS-C, a clustering-oriented formulation that averages distance matrices and learns reduced-support representations via a consensus Gromov-Wasserstein transport. Experiments on synthetic and real-world datasets show that the proposed framework yields stable and geometrically meaningful embeddings.

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