AI RESEARCH
Structure-Preserving Multi-View Embedding Using Gromov-Wasserstein Optimal Transport
arXiv CS.LG
•
ArXi:2604.02610v1 Announce Type: cross 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 Gromo-Wasserstein (GW) optimal transport.