Canonical correlation analysis (CCA) is a way of measuring the linear
relationship between two multidimensional variables. It finds two bases,
one for each variable, that are optimal with respect to correlations and,
at the same time, it finds the corresponding correlations. In other words,
it finds the two bases in which the correlation matrix between the variables
is diagonal and the correlations on the diagonal are maximized. The dimensionality
of these new bases is equal to or less than the smallest dimensionality
of the two variables.
For more information on CCA, please read my on-line tutorial (or the PDF version).
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