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:
Filters
Type
Experience
Scope
6 items
Randomized SVD Code [Python]
10 min
Beginner
Video
Application
This video describes the randomized singular value decomposition (rSVD) (Python code).
See MoreRandomized SVD Code [Matlab]
9 min
Beginner
Video
Application
This video describes the randomized singular value decomposition (rSVD) (Matlab code).
See MoreSVD: 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 MoreSVD: Importance of Alignment [Matlab]
6 min
Beginner
Video
Application
This video describes the importance of aligning data when using the singular value decomposition (SVD) (Matlab code).
See MoreSVD: Image Compression [Python]
9 min
Beginner
Video
Application
This video describes how to use the singular value decomposition (SVD) for image compression in Python.
See MoreSVD: Image Compression [Matlab]
14 min
Beginner
Video
Application
This video describes how to use the singular value decomposition (SVD) for image compression in Matlab.
See More