This article needs additional citations for
verification. (July 2011) |
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]
This article needs additional citations for
verification. (July 2011) |
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]