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Singular Value Decomposition (SVD) (see Appendix A.6, Trucco & Verri)

Singular Value Decomposition (SVD) (see Appendix A.6, Trucco & Verri). CS485/685 Computer Vision Prof. George Bebis. SVD. Any real m x n matrix A can be decomposed uniquely: U is m x n and column orthonormal (U T U=I) D is n x n and diagonal

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Singular Value Decomposition (SVD) (see Appendix A.6, Trucco &amp; Verri)

Singular Value Decomposition (SVD) (see Appendix A.6, Trucco & Verri)