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.
Topic
Singular Value Decomposition (SVD)
This topic includes the following resources and journeys:
Type
Experience
Scope
Singular Value Decomposition (SVD): Overview
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 MoreRandomized SVD Code [Matlab]
This video describes the randomized singular value decomposition (rSVD) (Matlab code).
See MoreSVD: Image Compression [Python]
This video describes how to use the singular value decomposition (SVD) for image compression in Python.
See MoreSVD: Image Compression [Matlab]
This video describes how to use the singular value decomposition (SVD) for image compression in Matlab.
See MoreSVD Method of Snapshots
This video describes how to compute the singular value decomposition (SVD) using the method of snapshots, by Sirovich 1987.
See MoreLeast Squares Regression and the SVD
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: Importance of Alignment [Python]
This video describes the importance of aligning data when using the singular value decomposition (SVD) (Python code).
See MoreRandomized SVD Code [Python]
This video describes the randomized singular value decomposition (rSVD) (Python code).
See MoreSVD: Importance of Alignment [Matlab]
This video describes the importance of aligning data when using the singular value decomposition (SVD) (Matlab code).
See More