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Numerical analysis
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===Solving eigenvalue or singular value problems=== Several important problems can be phrased in terms of [[eigenvalue decomposition]]s or [[singular value decomposition]]s. For instance, the [[image compression|spectral image compression]] algorithm<ref>{{cite web |first1=C.J. |last1=Ogden |first2=T. |last2=Huff |title=The Singular Value Decomposition and Its Applications in Image Compression |date=1997 |work=Math 45 |publisher=College of the Redwoods |url=http://online.redwoods.cc.ca.us/instruct/darnold/laproj/Fall97/Tammie/tammie.pdf|archive-url=https://web.archive.org/web/20060925193348/http://online.redwoods.cc.ca.us/instruct/darnold/laproj/Fall97/Tammie/tammie.pdf |archive-date=25 September 2006 }}</ref> is based on the singular value decomposition. The corresponding tool in statistics is called [[principal component analysis]].
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