Regularized canonical correlation analysis

Regularized canonical correlation analysis is a way of using ridge regression to solve the singularity problem in the cross-covariance matrices of canonical correlation analysis. By converting $\operatorname {cov} (X,X)$ and $\operatorname {cov} (Y,Y)$ into $\operatorname {cov} (X,X)+\lambda I_{X}$ and $\operatorname {cov} (Y,Y)\lambda I_{Y}$, it ensures that the above matrices will have reliable inverses.