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 m × n matrix via an extension of the polar decomposition.
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Singular Value Decomposition (SVD)
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Randomized SVD Code [Python]
10 min
Beginner
Video
Application
This video describes the randomized singular value decomposition (rSVD) (Python code).
See MoreSVD: Eigenfaces 4 [Matlab]
6 min
Intermediate
Video
Application
This video describes how the singular value decomposition (SVD) can be used to efficiently represent human faces, in the so-called "eigenfaces" (Matlab code, part 4).
See MoreSVD: Optimal Truncation [Matlab]
12 min
Intermediate
Video
Application
This video describes how to optimally truncate the singular value decomposition (SVD) for noisy data (Matlab code).
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