no code implementations • 3 Feb 2024 • Hugues van Assel, Cédric Vincent-Cuaz, Nicolas Courty, Rémi Flamary, Pascal Frossard, Titouan Vayer
Unsupervised learning aims to capture the underlying structure of potentially large and high-dimensional datasets.
no code implementations • 5 Oct 2023 • Hugues van Assel, Cédric Vincent-Cuaz, Titouan Vayer, Rémi Flamary, Nicolas Courty
We present a versatile adaptation of existing dimensionality reduction (DR) objectives, enabling the simultaneous reduction of both sample and feature sizes.
no code implementations • 4 Oct 2023 • Hugues van Assel, Titouan Vayer, Remi Flamary, Nicolas Courty
Regularising the primal formulation of optimal transport (OT) with a strictly convex term leads to enhanced numerical complexity and a denser transport plan.
no code implementations • 31 Jan 2022 • Hugues van Assel, Thibault Espinasse, Julien Chiquet, Franck Picard
Most popular dimension reduction (DR) methods like t-SNE and UMAP are based on minimizing a cost between input and latent pairwise similarities.