In statistics, canonical-correlation analysis (CCA), also called canonical variates analysis, is a way of inferring information from cross-covariance...

Regularized canonical correlation analysis is a way of using ridge regression to solve the singularity problem in the cross-covariance matrices of canonical correlation...

criterion variables by the canonical correlations ρ1, ρ2, ..., and by the sets of canonical weights C and D. Canonical analysis belongs to a group of methods...

In statistics, the generalized canonical correlation analysis (gCCA), is a way of making sense of cross-correlation matrices between the sets of random...

canonical) version of bivariate correlation. Redundancy analysis (RDA) is similar to canonical correlation analysis but allows the user to derive a specified...

to any form of multiple regression analysis, factor analysis, canonical correlation analysis, discriminant analysis, as well as more general families of...

principal component analysis (PCA), independent component analysis (ICA), linear discriminant analysis (LDA) and canonical correlation analysis (CCA). Multilinear...

non-parametric alternative Discriminant function analysis Canonical correlation analysis Multivariate analysis of variance (Wikiversity) Repeated measures...

eigenvectors of a slightly different matrix. PCA is also related to canonical correlation analysis (CCA). CCA defines coordinate systems that optimally describe...

extracting the correlation coefficient between two sets of stochastic variables is nontrivial, in particular where Canonical Correlation Analysis reports degraded...