The relationship between canonical correlation analysis and minimum squared error classifier

Canonical correlation analysis (CCA) has recently attracted great attention and many experimental results have illustrated its effectiveness. In this paper, we study the relationship between CCA classifier and minimum squared error (MSE) classifier. It helps us look into the nature of CCA classifier. In traditional CCA method, the class-membership matrix is deliberately coded in full rank. Under this case, we will prove CCA is equivalent to MSE classifier. It is also shown that even the class-membership matrix is centered and thus not in full rank, CCA is equivalent to Fisher linear discriminant analysis (FDA). Some experiments are presented to verify the results.