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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.23912 |
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| _version_ | 1866915960548491264 |
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| author | Eufrazio, Rafael Pereira Montesuma, Eduardo Fernandes Cavalcante, Charles Casimiro |
| author_facet | Eufrazio, Rafael Pereira Montesuma, Eduardo Fernandes Cavalcante, Charles Casimiro |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_23912 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Gromov-Wasserstein Methods for Multi-View Relational Embedding and Clustering Eufrazio, Rafael Pereira Montesuma, Eduardo Fernandes Cavalcante, Charles Casimiro Machine Learning 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. |
| title | Gromov-Wasserstein Methods for Multi-View Relational Embedding and Clustering |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2604.23912 |