Riemannian Metric Learning via Optimal Transport

We introduce an optimal transport-based model for learning a metric tensor from cross-sectional samples of evolving probability measures on a common Riemannian manifold. We neurally parametrize the metric as a spatially-varying matrix field and efficiently optimize our model's objective using b...

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Bibliographic Details
Main Author: Scarvelis, Christopher
Other Authors: Solomon, Justin
Format: Thesis
Published: Massachusetts Institute of Technology 2023
Online Access:https://hdl.handle.net/1721.1/147268
https://orcid.org/0000-0001-8516-6189