<ul data-eligibleForWebStory="false"><li>Researchers propose a method to enhance distributional robustness in Principal Component Analysis by using Wasserstein distances.</li><li>They introduce a distributionally robust optimization model for PCA to handle uncertainty in probability distribution.</li><li>The proposed approach involves a smoothing manifold proximal gradient algorithm to tackle the challenging nonsmooth optimization problem.</li><li>Numerical experiments confirm the efficacy and scalability of the algorithm, emphasizing the importance of the DRO model for PCA.</li></ul>

Enhancing Distributional Robustness in Principal Component Analysis by Wasserstein Distances

Discover more