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Definition of "singular value decomposition (SVD)" |
useful decomposition method for matrix inverse and pseudoinverse problems, including the least-squared solution of overdetermined systems. SVD represents the matrix A in the form A = U.Λ^1/2 .V , where Λ is a diagonal matrix whose entries are the singular values of A, and U and V are the row and column eigenvector systems of A. Any matrix can be represented in this way. In image processing, SVD has been applied to coding, to image filtering, and to the approximation of non-separable 2-D point spread functions by two orthogonal 1-D impulse responses. |
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