AI RESEARCH
Gromov-Wasserstein Methods for Multi-View Relational Embedding and Clustering
arXiv CS.LG
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ArXi:2604.23912v1 Announce Type: new Learning low-dimensional representations from multi-view relational data is challenging when underlying geometries differ across views. We propose Bary-GWMDS, a Gromo-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