Singular Value Decomposition (SVD)
- is a generalization of eigen-decomposition, decomposing rectangular matrices
- related to singular values
- is like a “data-driven” Fast Fourier Transform (FFT)
if 𝐴𝑥 = 𝜆𝑥 & ||𝑥|| = 1, then:
- ||𝐴𝑥|| = ||𝜆𝑥||
- ||𝐴𝑥|| = |𝜆| ||𝑥||
- ||𝐴𝑥|| = |𝜆|
SVD - Definition (Real and/or Complex Matrix)
The SVD of an 𝑚✕𝑛 real and/or complex matrix 𝐴 with rank 𝑟 is:
- 𝐴 = 𝑈𝛴𝑉*
where:
- 𝑈 - 𝑚✕𝑚 real or complex unitary matrix
- 𝛴 - 𝑚✕𝑛rectangular matrix with diagonal entries of the first 𝑟 singular values of 𝐴 (𝜎1 ≥ 𝜎2 ≥ … ≥ 𝜎𝑟)
- 𝑉 - 𝑛✕𝑛 real or complex unitary matrix (𝑉* - complex conjugate of 𝑉)
SVD - Definition (Real Matrix)
If 𝐴 is real, 𝑈 and 𝑉*=𝑉𝑇 are real orthogonal matrices (where 𝑉𝑇 is the transpose)
- 𝐴 = 𝑈𝛴𝑉𝑇
where:
- 𝑈 - orthogonal 𝑚✕𝑚unitary matrix. normalized eigenvectors of the symmetric matrix 𝐴𝐴𝑇 (𝑖.𝑒. 𝑈𝑇𝑈=𝐼) columns of 𝑈 are called the left singular vectors of 𝐴
- 𝑉 - orthogonal 𝑛✕𝑛 unitary matrix. normalized eigenvectors of the symmetric matrix 𝐴𝑇𝐴 (𝑖.𝑒. 𝑉𝑇𝑉=𝐼) columns of 𝑉 are called the right singular vectors of 𝐴
- 𝛴 - diagonal matrix of singular values
- 𝛴 = 𝐷(1/2) where 𝐷 is the diagonal matrix of the eigenvalues of matrix 𝐴𝐴𝑇and 𝐴𝑇𝐴
derivation:
Click here to expand...
let {𝐴𝑣1, …, 𝐴𝑣𝑟} be an orthogonal basis for column-space of 𝐴
normalize each 𝐴𝑣𝑖 to obtain orthonormal basis {𝑢1, …, 𝑢𝑟} for column-space of 𝐴
now extend {𝑢1, …, 𝑢𝑟} to an orthonormal basis {𝑢1, …, 𝑢𝑚} of ℝ𝑚
SVD - 4 Fundamental Subspaces
---reduced-svd/svd-visual.png)
The Invertible Matrix Theorem
let 𝐴 be 𝑛✕𝑛 matrix. then the following statements are equivalent to the statement that 𝐴 is an invertible matrix:
- (𝐶𝑜𝑙𝐴)⟂= {0}
- (N𝑢𝑙𝑙𝐴)⟂ = ℝ𝑛
- 𝑅𝑜𝑤𝐴 = ℝ𝑛
- 𝐴 has 𝑛 non-zero singular values
SVD - Reduced SVD & Pseudo Inverse (Moore-Penrose Inverse)
Indent
𝑈𝑟𝐷𝑉𝑟𝑇is called reduced SVD
pseudoinverse (Moore-Penrose Inverse) of 𝐴:
- 𝐴†= 𝑉𝑟𝐷-1𝑈𝑟𝑇
application to least-squares solution
given the equation 𝐴𝑥 = 𝑏, use pseudoinverse of 𝐴
- 𝑥̂ = 𝐴†𝑏
- 𝑥̂ = 𝑉𝑟𝐷-1𝑈𝑟𝑇𝑏
then from SVD:
- 𝐴𝑥̂ = 𝑈𝑟𝐷𝑉𝑟𝑇𝑉𝑟𝐷-1𝑈𝑟𝑇𝑏
- 𝐴𝑥̂ = 𝑈𝑟𝑈𝑟𝑇𝑏
SVD - Intuition (As Linear Transformations)
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When 𝐴 is a Square 𝑚✕𝑚 Real Matrix
When 𝐴 is a Rectangular 𝑚✕𝑛 Real Matrix
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SVD - 4 Fundamental Subspaces---reduced-svd/reduced-svd.png)
---reduced-svd/singular-value-decomposition.png)