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Answer» What is Singular Value Decomposition mean? In linear algebra, the singular value decomposition (SVD) is a factorization of a real or complex matrix. It generalizes the eigendecomposition of a square normal matrix with an orthonormal eigenbasis to any m × n {\displaystyle m\times n} matrix. It is related to the polar decomposition. Specifically, the singular value decomposition of an m × n {\displaystyle m\times n} complex matrix M is a factorization of the form U Σ V ∗ {\displaystyle \mathbf {U\Sigma V^{*}} } , where U is an m × m {\displaystyle m\times m} complex unitary matrix, Σ {\displaystyle \mathbf {\Sigma } } is an m × n {\displaystyle m\times n} rectangular diagonal matrix with non-negative real numbers on the diagonal, and V is an n × n {\displaystyle n\times n} complex unitary matrix. If M is real, U and V can also be guaranteed to be real orthogonal matrices. In such contexts, the SVD is often denoted U Σ V T {\displaystyle \mathbf {U\Sigma V^{T}} } . The diagonal entries σ i reference
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