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000116112 005__ 20190316234118.0
000116112 02470 $$2ISI$$a000257456702292
000116112 037__ $$aCONF
000116112 245__ $$aAverage case analysis of sparse recovery with Thresholding: New bounds based on average dictionary coherence
000116112 260__ $$c2008
000116112 269__ $$a2008
000116112 336__ $$aConference Papers
000116112 520__ $$aThis paper analyzes the performance of the simple thresholding algorithm for sparse signal representations. In particular, in order to be more realistic we introduce a new probabilistic signal model which assumes randomness for both the amplitude and also the location of nonzero entries. Based on this model we show that thresholding in average can correctly recover signals for much higher sparsity levels than was previously reported. The bounds we obtain in this paper are based on a new concept of average dictionary coherence and are shown to be much sharper than in former works.
000116112 6531_ $$aSparse representation
000116112 6531_ $$aRedundant dictionary
000116112 6531_ $$aCumulative and average coherence
000116112 6531_ $$aThresholding
000116112 6531_ $$aLTS2
000116112 6531_ $$alts2
000116112 700__ $$0242923$$g171628$$aGolbabaee, Mohammad
000116112 700__ $$aVandergheynst, Pierre$$g120906$$0240428
000116112 7112_ $$d30.03.2008 - 05.04.2008$$cLas Vegas, Nevada$$aIEEE Int. Conf. on Acoustics, Speech & Signal Processing (ICASSP), 2008
000116112 8564_ $$uhttp://www.icassp2008.org/$$zURL
000116112 8564_ $$uhttps://infoscience.epfl.ch/record/116112/files/golbabaee.pdf$$zn/a$$s150648
000116112 909C0 $$xU10380$$0252392$$pLTS2
000116112 909CO $$ooai:infoscience.tind.io:116112$$qGLOBAL_SET$$pconf$$pSTI
000116112 937__ $$aEPFL-CONF-116112
000116112 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000116112 980__ $$aCONF