A scalable rayleigh-ritz style method for large scale Canonical Correlation Analysis

In this paper, we propose a novel inverse-free iterative algorithm for efficiently solving the generalized eigenvalue problem in Canonical Correlation Analysis (CCA). Compared with the state-of-the-art approach of reformulating it as a regression problem, our method is more efficient and can find the exact solution to the original generalized eigenvalue problem under a milder condition. Numerical experiments on several large-scale datasets illustrate the superior performance of the proposed method.