Singular Value Decomposition
This is an old revision of this page, as edited by Aschlafly (talk | contribs) at 04:26, April 26, 2010. It may differ significantly from current revision.
Singular value decomposition, or SVD, consists of the reduction of a singular matrix into the product of orthogonal and square matrices.
Discovered in the 19th century, this technique has recently discovered applications in signal error-reduction and in watermarking.