Singular Value Decomposition (SVD)
Singular value Decomposition t i i r i ii A USV T ¦ S u v 1 This m by n matrix u i vT i is the product of a column vector u i and the transpose of column vector v i. It has rank 1. Thus A is a weighted summation of r rank-1 matrices. Note: u i and v i are the i …
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