This repo implements several applications of the proposed generalized Bures-Wasserstein (GBW) geometry on symmetric positive definite matrices.

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Deep LearningGBW
Overview

GBW

This repo implements several applications of the proposed generalized Bures-Wasserstein (GBW) geometry on symmetric positive definite matrices.

Applications include 1) Riemannian optimization for log-det maximization and Gaussian mixture model; 2) Geometry-aware PCA on SPD manifold with the GBW geometry.

To run the code, you need to download the manopt toolbox from https://www.manopt.org/ and place it in the same parent folder.

If you find the code useful, please kindly cite the following paper: arXiv:2110.10464.

Owner
Andi Han
Andi Han
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