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Lifting PCA in the Wasserstein Space

Jérémie Bigot, Clément Bonet · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.29679 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

主な確率分布プロセッサによって確率分布プロセッサ(PCA)は、統計的次元減少のための基本的なツールであるが、分布評価データのためのカノニカル拡張は、最適な輸送の枠組み内で明らかにされていない。 ウォーサーシュタインバリエンテールおよびウォーサーシュタイン最小平方回帰のカノニカル拡張は、ウォーサーシュタイン空間で統計的次元減少および線形回帰のための基本的なツールであるが、PCAのための同様の原理は未調査されていない。 この論文では、我々は、ウォーサーシュタイン最小平方回帰のウォーサーシュタインバリエンテールおよびウ

Machine translation (Meta M2M100); the English original follows.

Abstract

Principal Component Analysis (PCA) is a fundamental tool for statistical dimension reduction, yet no canonical extension to distribution-valued data has emerged within the framework of optimal transport. While Wasserstein barycenters and Wasserstein least-squares regression provide canonical liftings of Euclidean averaging and linear regression in the Wasserstein space, an analogous principle for PCA has remained unexplored. In this paper, we introduce Lifted PCA, a canonical extension of Euclidean PCA to Wasserstein space, obtained through the framework of lifting functionals in the space of probability measures by convex extension. Starting from the classical variational formulations of PCA on the Stiefel and Grassmann manifolds, we derive two equivalent minimization problems over probability measures supported on these manifolds that correspond to Wasserstein barycenters with generalized costs. We establish an exact analogue of the equivalence between the Stiefel and Grassmann formulations of Euclidean PCA, preserving PCA's original variational structure under lifting. We further derive dual formulations and optimality conditions, and establish equivalences with multimarginal optimal transport and matching-for-teams problems. We also establish statistical consistency for empirical estimators and develop computational algorithms based on the Wasserstein barycenter formulation over the Stiefel manifold. Whereas Euclidean PCA summarizes vectors through projections onto an optimal low-dimensional linear subspace, Lifted PCA summarizes probability distributions through an optimal probability measure supported on multiple low-dimensional subspaces. Numerical experiments on distributional datasets illustrate the practical performance of the proposed methodology and its benefits for distributional data analysis.

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Topics: Mathematics, Probability measure, Euclidean space, Dimension (graph theory), Euclidean geometry, Applied mathematics

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