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: Importance of Alignment [Python]](/sites/default/files/styles/search_resulkts/public/2020-12/maxresdefault_422.jpg?itok=Ix4uqD1g)
SVD: Importance of Alignment [Python]
6 min
Beginner
Video
Application
This video describes the importance of aligning data when using the singular value decomposition (SVD) (Python code).
See More![SVD: Eigen Action Heros [Matlab]](/sites/default/files/styles/search_resulkts/public/2020-12/maxresdefault_411.jpg?itok=YrhhkBXx)
SVD: Eigen Action Heros [Matlab]
16 min
Intermediate
Video
Application
This video describes how the singular value decomposition (SVD) can be used to efficiently represent human faces. In this example, we represent action heros (Matlab).
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Least Squares Regression and the SVD
5 min
Beginner
Video
Theory
This video describes how the SVD can be used to solve linear systems of equations. In particular, it is possible to solve nonsquare systems (overdetermined or underdetermined) via least...
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