data science
A brief history of the Singular value decomposition of matrices with some applications
The singular value decomposition (SVD) of a matrix is a fundamental result in linear algebra and its applications in data science.
It produces good bases for the four fundamental subspaces associated with a real matrix A: the row and column spaces, the nullspace, and the nullspace of its transpose of A. The SVD produces a method to find the best approximation of A by matrices of lower rank. This Theorem has applications to data compression.