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?



Browse other questions tagged or ask your own question.