3 papers
cs.LG2023
Interpolating between Clustering and Dimensionality Reduction with Gromov-Wasserstein
Hugues Van Assel, Cédric Vincent-Cuaz, Titouan Vayer +2
We present a versatile adaptation of existing dimensionality reduction (DR) objectives, enabling the simultaneous reduction of both sample and feature sizes. Correspondances betwee…
cs.LG2023
Optimal Transport with Adaptive Regularisation
Hugues Van Assel, Titouan Vayer, Remi Flamary +1
Regularising the primal formulation of optimal transport (OT) with a strictly convex term leads to enhanced numerical complexity and a denser transport plan. Many formulations impo…
stat.ML2023
Entropic Wasserstein Component Analysis
Antoine Collas, Titouan Vayer, Rémi Flamary +1
Dimension reduction (DR) methods provide systematic approaches for analyzing high-dimensional data. A key requirement for DR is to incorporate global dependencies among original an…