Applications of singular-value decomposition (SVD)
(Citations: 4)
Let A be an m × n matrix with m ≥ n. Then one form of the singular-value decomposition of A is A = U T ΣV, where U and V are orthogonal and Σ is square diagonal. That is, UU T = Irank(A), VV T = Irank(A) ,U is rank(A) × m, V is rank(A) × n and