Nowadays, Regularized canonical correlation analysis is a topic that has gained great relevance in today's society. Over time, Regularized canonical correlation analysis has acquired a fundamental role in different aspects of daily life, whether in the workplace, in the technological context, in personal life or in any other area. The importance of Regularized canonical correlation analysis has transcended barriers and prejudices, becoming a topic of general interest that requires analysis and reflection. In this article, we will explore different perspectives on Regularized canonical correlation analysis and its impact on life today.
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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". It has been suggested for use in the analysis of functional neuroimaging data as such data are often singular. It is possible to compute the regularized canonical vectors in the lower-dimensional space.