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On the Wasserstein Geodesic Principal Component Analysis of probability measures

arXiv CS Tuesday 09 June 2026, 04:00 UTC By Nina Vesseron, Elsa Cazelles, Alice Le Brigant, Thierry Klein 1 min read
Key Points

arXiv:2506.04480v2 Announce Type: replace-cross Abstract: 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. We first address the case of a collection of Gaussian distributions, and show how to lift the computations in the space of invertible linear maps.

arXiv:2506.04480v2 Announce Type: replace-cross Abstract: 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. We first address the case of a collection of Gaussian distributions, and show how to lift the computations in the space of invertible linear maps. For the more general setting of absolutely continuous probability measures, we leverage a novel approach to parameterizing geodesics in Wasserstein space with neural networks. Finally, we compare to classical tangent PCA through various examples and provide illustrations on real-world datasets.
the Wasserstein Geodesic Principal Component Analysis (LOCATION) Geodesic Principal Component Analysis (ORG) the Otto-Wasserstein (ORG) Wasserstein (LOCATION) PCA (ORG)
Originally published by arXiv CS Read original →

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