000087170 001__ 87170
000087170 005__ 20190316233749.0
000087170 037__ $$aREP_WORK
000087170 245__ $$aSparse Approximation by Linear Programming: Measuring the Error with the $ell_1$ Norm
000087170 269__ $$a2005
000087170 260__ $$c2005$$aEcublens
000087170 336__ $$aReports
000087170 500__ $$aITS
000087170 520__ $$aIn this report we study the problem of sparse signal approximation over redundant dictionaries. We focus our attention on the minimization of a cost function where the error is measured using a l1 norm. We show a constructive equivalence between this minimization and Linear Programming. A recovery condition is then proved and finally we provide an example of the use of such a technique for denoising.
000087170 6531_ $$aLTS2
000087170 700__ $$0241529$$g141038$$aGranai, L.
000087170 700__ $$aVandergheynst, P.$$g120906$$0240428
000087170 8564_ $$uhttps://infoscience.epfl.ch/record/87170/files/Granai2005_1295.pdf$$zn/a$$s131795
000087170 909C0 $$xU10380$$0252392$$pLTS2
000087170 909CO $$ooai:infoscience.tind.io:87170$$qGLOBAL_SET$$pSTI$$preport
000087170 937__ $$aEPFL-REPORT-87170
000087170 973__ $$sPUBLISHED$$aEPFL
000087170 970__ $$aGranai2005_1295/LTS
000087170 980__ $$aREPORT