I am looking for a geometric interpretation of CCA. Especially one that relies on the fact we are doing singular value decomposition, which has the geometric interpretation of a rotation, scaling and another rotation. Why taking these two rotations from the SVD of the cross-correlation matrix gives the most correlated linear projections?
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2Can this help? http://stats.stackexchange.com/q/65692/3277 – ttnphns Sep 21 '16 at 11:12