Relative Wasserstein Angle and the Problem of the $W_2$-Nearest Gaussian Distribution

Abstract:Understanding the distributional structure of high-dimensional datasets has become an important topic, yet direct visual characterization is difficult. In this work, we develop a geometric framework for characterizing the distributional structure of empirical datasets by quantifying their deviation from the Gaussian family under the geometry induced by optimal transport theory. Building on the cone structure of the relative translation invariant quadratic Wasserstein $(RW_2)$ space, we define two geometric quantities---the \emph{relative Wasserstein angle} and the \emph{orthogonal projection distance}---and show that they are well-defined because of the flat geometry of the filling cone between distributional rays. This formulation recasts the problem of measuring deviation from the Gaussian family as an orthogonal projection problem onto the Gaussian cone and reveals that the commonly used moment-matching Gaussian is, in general, not the $W_2$-nearest Gaussian to a non-Gaussian distribution. In one dimension, we derive closed-form expressions for the proposed quantities and extend closed-form expressions to several other location--scale families, including uniform, Laplace, and logistic distributions. In higher dimensions, we develop a numerical approximation method for the proposed quantities based on empirical optimal transport and covariance-shape optimization. Our experimental results show the empirical convergence and stability of the proposed methods and reveal that the $RW_2$ angle provides a robust and consistent measure of distributional non-Gaussianity. Moreover, these results provide empirical support for its potential use as an indicator of distributional heterogeneity.

Machine Learning (cs.LG)

arXiv:2601.22355 [cs.LG]

(or arXiv:2601.22355v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2601.22355 Focus to learn more arXiv-issued DOI via DataCite

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