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.
Singular Value Decomposition (SVD)
This topic includes the following resources and journeys:
This video describes how the singular value decomposition (SVD) can be used for matrix approximation.See More
This video presents a mathematical overview of the singular value decomposition (SVD).See More
This lectures discusses how the SVD captures dominant correlations in a matrix of data.See More