arXiv stat.ML
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Wasserstein Mahalanobis Distances for Recovering Latent Geometry
arXiv:2608.06560v1 Announce Type: cross Abstract: The Mahalanobis distance is a fundamental covariance-adapted metric for multivariate data and plays a central role in recovering latent geometry from nonlinear observations. We extend this principle from vector-valued data to probability measures by introducing a Wasser