Singular value decomposition
Matrix decomposition
Singular value decomposition
In linear algebra, the singular value decomposition (SVD) is a factorization of a real or complex matrix into a rotation, followed by a scaling, followed by another rotation. 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, and is a common means for implementing low-rank approximation for matrices. Specifically, the singular value decomposition of an m × n {\displaystyle m\times n} complex matrix M {\displaystyle \mathbf {M} } is a factorization of the form M = U Σ V ∗...
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