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| '''Singular values''' are | '''Singular values''' are a generalization of [[LinearAlgebra/EigenvaluesAndEigenvectors|eigenvalues]]. |
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| Any linear transformation can be rewritten as a rotation (i.e., from the original basis onto a convenient basis), a scaling and stretching, and another rotation (i.e., from the convenient basis onto the destination basis). | It is known that (for square matrices) there are [[LinearAlgebra/EigenvaluesAndEigenvectors|certain vectors]] which the transformation only scales: '''''A'''x = λx''. |
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| Therefore '''''A''' = '''UΣV'''^T^'', where the above descriptions correspond to '''''V''''', '''''Σ''''', and '''''U''''' in that order. | Similarly, for any matrix '''''A''''' of size ''m x n'', there are certain vectors ''u,,i,,'' in the column space that correspond to certain vectors ''v,,i,,'' in the row space. They correspond such that '''''A'''v = σu''. And by composing these vectors and values together: '''''AV''' = '''UΣ'''''. |
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| Singular values are the square roots of [[LinearAlgebra/EigenvaluesAndEigenvectors|eigenvalues]]. Specifically, a matrix '''''A''''' has singular values (''σ'') corresponding to the eigenvalues of '''''A'''^T^'''A'''''. | Singular values are square roots of [[LinearAlgebra/EigenvaluesAndEigenvectors|eigenvalues]]. Specifically, a matrix '''''A''''' has singular values equal to the square roots of the eigenvalues of '''''A'''^T^'''A''''': ''σ = √λ''. |
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| Matrices of all sizes have singular values. For a matrix of size ''m x n'', there are at most ''min{m,n}'' singular values. Furthermore, the number of non-zero singular values is equal to the [[LinearAlgebra/Rank|rank]] of '''''A'''''. These singular values are the numbers on the diagonal of '''''Σ'''''. Naturally '''''Σ''''' matches '''''A''''' as a ''m x n'' matrix. |
Matrices of all sizes have singular values. The number of non-zero singular values is equal to the [[LinearAlgebra/Rank|rank]] of '''''A'''''. |
Singular Values
Singular values are a generalization of eigenvalues.
Contents
Introduction
It is known that (for square matrices) there are certain vectors which the transformation only scales: Ax = λx.
Similarly, for any matrix A of size m x n, there are certain vectors ui in the column space that correspond to certain vectors vi in the row space. They correspond such that Av = σu. And by composing these vectors and values together: AV = UΣ.
Description
Singular values are square roots of eigenvalues. Specifically, a matrix A has singular values equal to the square roots of the eigenvalues of ATA: σ = √λ.
Matrices of all sizes have singular values. The number of non-zero singular values is equal to the rank of A.