Gromov-Wasserstein Methods for Multi-View Relational Embedding and Clustering
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.
Cite
@article{arxiv.2604.23912,
title = {Gromov-Wasserstein Methods for Multi-View Relational Embedding and Clustering},
author = {Rafael Pereira Eufrazio and Eduardo Fernandes Montesuma and Charles Casimiro Cavalcante},
journal= {arXiv preprint arXiv:2604.23912},
year = {2026}
}
Comments
This manuscript is currently under review at the XLIV Simposio Brasileiro de Telecomunicacoes e Processamento de Sinais - SBrT (Brazilian Symposium on Telecommunications and Signal Processing ) 2026