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math.ST2026
Covariance and Principal Component Analysis on Riemannian Manifolds and Graphs
Luiz Hartmann, Wenwen Li, Washington Mio
We develop notions of covariance and principal component analysis (PCA) for probability measures on Riemannian manifolds of bounded geometry and a discrete counterpart for distribu…
math.MG2026
The Observable Wasserstein Distance
Edivaldo Lopes dos Santos, Leandro Vicente Mauri, Washington Mio +1
We introduce the observable Wasserstein distance, a framework for deriving lower bounds on the Wasserstein distance between probability measures on Polish metric spaces, designed t…