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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SVD Method of Snapshots
4 min
Beginner
Video
Theory
This video describes how to compute the singular value decomposition (SVD) using the method of snapshots, by Sirovich 1987.
See MoreUnitary Transformations and the SVD [Python]
6 min
Intermediate
Video
Application
This video describes how the singular value decomposition (SVD) is related to unitary transformations, with Python code.
See MoreSVD: Eigenfaces 2 [Python]
10 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" (Python code, part 2).
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