On the Wasserstein Geodesic Principal Component Analysis of probability measures

ICLROral2026

Authors: Nina Vesseron, Elsa Cazelles, Alice Le Brigant, Klein
Affiliation: Ecole Nationale de la Statistique et de l'Administration Economique
Venue: ICLR 2026
Track: Oral

TL;DR

This paper focuses on Geodesic Principal Component Analysis (GPCA) on a collection of probability distributions using the Otto-Wasserstein geometry. The goal is to identify geodesic curves in the space of probability measures that best capture the modes of variation of the underlying dataset.

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