Singular value decomposition (SVD) is a matrix factorization method that generalizes the eigendecomposition of a square matrix (n x n) to any matrix (n x m) . General formula of SVD is: M=USVᵗ, where:
M-is original matrix we want to decompose
U-is left singular matrix (columns are left singular vectors). U columns contain eigenvectors of matrix MMᵗ
S-is a diagonal matrix containing singular (eigen)values
V-is right singular matrix (columns are right singular vectors). V columns contain eigenvectors of matrix MᵗM
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