An Online Riemannian PCA for Stochastic Canonical Correlation Analysis.

ABSTRACT: We present an efficient stochastic algorithm (RSG+) for canonical correlation analysis (CCA) using a reparametrization of the projection matrices. We show how this reparametrization (into structured matrices), simple in hindsight, directly presents an opportunity to repurpose/adjust mature techniques for numerical optimization on Riemannian manifolds. Our developments nicely complement existing methods for this problem which either require O(d ³) time complexity per iteration with O(1t) convergence rate (where d is the dimensionality) or only extract the top 1 component with O(1t) convergence rate. In contrast, our algorithm offers an improvement: it achieves O(d ² k) runtime complexity per iteration for extracting the top

SUBMITTER: Meng Z 

PROVIDER: S-EPMC9216218 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature