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Applications of singular-value decomposition (SVD)

Applications of singular-value decomposition (SVD),10.1016/j.matcom.2004.05.005,Mathematics and Computers in Simulation,Alkiviadis G. Akritas,Gennadi

Applications of singular-value decomposition (SVD)   (Citations: 4)
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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
Journal: Mathematics and Computers in Simulation , vol. 67, no. 1-2, pp. 15-31, 2004
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