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Singular value decomposition'', or SVD, consists of the reduction of a singular matrix into the product of orthogonal and square matrices.

'''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]].
[[Category:mathematics]]
[[Category:linear algebra]]

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Singular Value Decomposition (view source)

Revision as of 04:26, April 26, 2010

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