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Singular Value Decomposition (SVD) is a decomposition (factorization) of rectangular real or complex matrix into the product of a unitary rotation matrix, a diagonal scaling matrix, and a second unitary rotation matrix.

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such that A = U * C * transposed(X) B = V * S * transposed(X) where C and S are diagonal matrices containing the singular values of A and B respective. (Explanation from Matlab) With SVD
asked Jul 30 '13 by Matthias Munz