Regularized canonical correlation analysis

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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 and into and , it ensures that the above matrices will have reliable inverses.

The idea probably dates back to Hrishikesh D. Vinod's publication in 1976 where he called it "Canonical ridge".[1][2] It has been suggested for use in the analysis of functional neuroimaging data as such data are often singular.[3] It is possible to compute the regularized canonical vectors in the lower-dimensional space.[4]

References

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