[2604.02610] Structure-Preserving Multi-View Embedding Using Gromov-Wasserstein Optimal Transport

Statistics > Machine Learning arXiv:2604.02610 (stat) [Submitted on 3 Apr 2026] Title:Structure-Preserving Multi-View Embedding Using Gromov-Wasserstein Optimal Transport Authors:Rafael Pereira Eufrazio, Eduardo Fernandes Montesuma, Charles Casimiro Cavalcante View a PDF of the paper titled Structure-Preserving Multi-View Embedding Using Gromov-Wasserstein Optimal Transport, by Rafael Pereira Eufrazio and 2 other authors View PDF HTML (experimental) Abstract:Multi-view data analysis seeks to integrate multiple representations of the same samples in order to recover a coherent low-dimensional structure. Classical approaches often rely on feature concatenation or explicit alignment assumptions, which become restrictive under heterogeneous geometries or nonlinear distortions. In this work, we propose two geometry-aware multi-view embedding strategies grounded in Gromov-Wasserstein (GW) optimal transport. The first, termed Mean-GWMDS, aggregates view-specific relational information by averaging distance matrices and applying GW-based multidimensional scaling to obtain a representative embedding. The second strategy, referred to as Multi-GWMDS, adopts a selection-based paradigm in which multiple geometry-consistent candidate embeddings are generated via GW-based alignment and a representative embedding is selected. Experiments on synthetic manifolds and real-world datasets show that the proposed methods effectively preserve intrinsic relational structure across views. These res...