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.

References

Leurgans, S. E., Moyeed, R. A., and Silverman, B. W. (1993). Canonical correlation analysis when the data are curves. Journal of the Royal Statistical Society. Series B (Methodological), 55(3), 725–740.