singular value decomposition

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In linear algebra, the singular value decomposition (SVD) is a factorization of a real or complex matrix that generalizes the eigendecomposition of a square normal matrix to any ${\displaystyle m\times n}$ matrix via an extension of the polar decomposition.


Mathematical applications of the SVD include computing the pseudoinverse, matrix approximation, and determining the rank, range, and null space of a matrix. The SVD is also extremely useful in all areas of science, engineering, and statistics, such as signal processing, least squares fitting of data, and process control.

Animated illustration of the SVD of a 2D, real shearing matrix M. First, we see the unit disc in blue together with the two canonical unit vectors. We then see the actions of M, which distorts the disk to an ellipse. The SVD decomposes M into three simple transformations: an initial rotation V^*, a scaling ${\displaystyle \mathbf {\Sigma } }$ along the coordinate axes, and a final rotation U. The lengths σ₁ and σ₂ of the semi-axes of the ellipse are the singular values of M, namely Σ_1,1 and Σ_2,2.

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https://en.wikipedia.org/wiki/Singular_value_decomposition