Editing Generalized canonical correlation (section)

{{Short description|Generalized CCA}}

In [[statistics]], the '''generalized [[canonical correlation]] analysis''' (gCCA), is a way of making sense of [[cross-correlation]] matrices between the sets of random variables when there are more than two sets. While a conventional CCA generalizes [[principal component analysis]] (PCA) to two sets of random variables, a gCCA  generalizes PCA to more than two sets of random variables. 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.