Difference between revisions of "Singular Value Decomposition"
Jump to navigation
Jump to search
m |
DavidB4-bot (talk | contribs) (→top: Category) |
||
| (One intermediate revision by the same user not shown) | |||
| Line 2: | Line 2: | ||
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.