Matrix singular value decomposition to a linear combination of rank one partial isometries
Petero Kwizera · AIP conference proceedings · 2012
This paper starts with the proof of a theorem relating Matrix Singular Value Decomposition (SVD) to a matrix expansion as a nonnegative linear combination of mutually orthogonal rank one partial isometries. This is then directly related to image compression. Image compression results are indicated.