Difference between revisions of "Singular Value Decomposition"
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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.) |
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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]]. | Discovered in the 19th century, this technique has recently discovered applications in signal error-reduction and in [[watermarking]]. | ||
| − | [[Category: | + | [[Category:Mathematics]] |
| − | [[Category: | + | [[Category:Linear Algebra]] |
Latest revision as of 14:36, July 28, 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.