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Generalized canonical correlation
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<p>In <a href="/facts/Statistics/DNPsKGYU">statistics</a>, the generalized <a href="/facts/Canonical_correlation/LuX8FltG">canonical correlation</a> analysis (gCCA), is a way of making sense of <a href="/facts/Cross-correlation/Fs1hY3br">cross-correlation</a> matrices between the sets of random variables when there are more than two sets. While a conventional CCA generalizes <a href="/facts/Principal_component_analysis/pCbVTqdR">principal component analysis</a> (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. </p>