Difference between revisions of "Singular Value Decomposition"
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Discovered in the 19th century, this technique has recently discovered applications in signal error-reduction and in [[watermarking]]. | Discovered in the 19th century, this technique has recently discovered applications in signal error-reduction and in [[watermarking]]. | ||
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Revision as of 13:33, July 11, 2016
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.