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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Singular Value Decomposition (SVD): Overview
6 min
Beginner
Video
Theory
This video presents an overview of the singular value decomposition (SVD), which is one of the most widely used algorithms for data processing, reduced-order modeling, and high-dimensional...
See MoreLeast 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...
See MoreSVD 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.
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