Efficient kernel canonical correlation analysis using Nyström approximation

Abstract

The main contribution of this paper is the derivation of non-asymptotic convergence rates for Nyström kernel canonical correlation analysis (CCA) in a setting of statistical learning. Our theoretical results reveal that, under certain conditions, Nyström kernel CCA can achieve a convergence rate comparable to that of the standard kernel CCA, while offering significant computational savings. This finding has important implications for the practical application of kernel CCA, particularly in scenarios where computational efficiency is crucial. Numerical experiments are provided to demonstrate the effectiveness of Nyström kernel CCA.