3 papers
math.OC2026
Wasserstein Mahalanobis Distances for Recovering Latent Geometry
Chuxiangbo Wang, Shiying Li, Caroline Moosmüller
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 ext…
math.AT2025
Persistent Homology for Labeled Datasets: Gromov-Hausdorff Stability and Generalized Landscapes
Yaoying Fu, Evgeniya Lagoda, Shiying Li +3
Techniques from metric geometry have become fundamental tools in modern mathematical data science, providing principled methods for comparing datasets modeled as finite metric spac…
math.MG2025
Metric properties of partial and robust Gromov-Wasserstein distances
Jannatul Chhoa, Michael Ivanitskiy, Fushuai Jiang +4
The Gromov-Wasserstein (GW) distances define a family of metrics, based on ideas from optimal transport, which enable comparisons between probability measures defined on distinct m…