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 cov ⁡ ( X , X ) {\displaystyle \operatorname {cov} (X,X)} and cov ⁡ ( Y , Y ) {\displaystyle \operatorname {cov} (Y,Y)} into cov ⁡ ( X , X ) + λ I X {\displaystyle \operatorname {cov} (X,X)+\lambda I_{X}} and cov ⁡ ( Y , Y ) + λ I Y {\displaystyle \operatorname {cov} (Y,Y)+\lambda I_{Y}} , it ensures that the above matrices will have reliable inverses.

Source: Wikipedia — Regularized canonical correlation analysis (CC BY-SA 4.0)

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 cov ⁡ ( X , X ) {\displaystyle \operatorname {cov} (X,X)} and cov ⁡ ( Y , Y ) {\displaystyle \operatorname {cov} (Y,Y)} into cov ⁡ ( X , X ) + λ I X {\displaystyle \operatorname {cov} (X,X)+\lambda I_{X}} and cov ⁡ ( Y , Y ) + λ I Y {\displaystyle \operatorname {cov} (Y,Y)+\lambda I_{Y}} , it ensures that the above matrices will have reliable inverses.

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Source: Wikipedia "Regularized canonical correlation analysis" · CC BY-SA 4.0

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