# Generalized canonical correlation

In

statistics , the**generalized**(gCCA), is a way of making sense ofcanonical correlation analysiscross-correlation matrices between the sets of random variables when there are more than two sets. It is a generalization of thePrincipal component analysis (PCA) to more than two sets of random variables like a conventional CCA also does the same thing for only two sets. The**canonical variables**represent those**common factors**that can be found by a large PCA of all of the transformed random variables after each set underwent its own PCA.**Applications**The

Helmert-Wolf blocking (HWB) method of estimatinglinear regression parameters can find an optimal solution only if all cross-correlations between the data blocks are zero. They can always be made to vanish by introducing a new regression parameter for each common factor. The gCCA method can be used for finding those harmful common factors that create cross-correlation between the blocks. However, no optimal HWB solution exists if the random variables do not contain enough information on all of the new regression parameters.**External links*** [

*http://factominer.free.fr/ FactoMineR*] (free exploratory multivariate data analysis software linked to R)

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