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Communication Dans Un Congrès Année : 2023

Interpolating between Clustering and Dimensionality Reduction with Gromov-Wasserstein

Résumé

We present a versatile adaptation of existing dimensionality reduction (DR) objectives, enabling the simultaneous reduction of both sample and feature sizes. Correspondances between input and embedding samples are computed through a semi-relaxed Gromov-Wasserstein optimal transport (OT) problem. When the embedding sample size matches that of the input, our model recovers classical popular DR models. When the embedding's dimensionality is unconstrained, we show that the OT plan delivers a competitive hard clustering. We emphasize the importance of intermediate stages that blend DR and clustering for summarizing real data and apply our method to visualize datasets of images.

Dates et versions

hal-04356797 , version 1 (20-12-2023)

Identifiants

Citer

Hugues Van Assel, Cédric Vincent-Cuaz, Titouan Vayer, Rémi Flamary, Nicolas Courty. Interpolating between Clustering and Dimensionality Reduction with Gromov-Wasserstein. NeurIPS OTML Workshop, Dec 2023, New Orleans, United States. ⟨hal-04356797⟩
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