000100902 001__ 100902
000100902 005__ 20190316233935.0
000100902 02470 $$2DAR$$a11532
000100902 02470 $$2ISI$$a000250371700015
000100902 037__ $$aARTICLE
000100902 245__ $$aAverage Performance Analysis for Thresholding
000100902 269__ $$a2007
000100902 260__ $$c2007
000100902 336__ $$aJournal Articles
000100902 520__ $$aIn this article is shown that with high probability the thresholding algorithm can recover signals that are sparse in a redundant dictionary as long as the {\it 2-Babel function} is growing slowly. This implies that it can succeed for sparsity levels up to the order of the ambient dimension. The theoretical bounds are illustrated with numerical simulations. As an application of the theory {\it sensing dictionaries} for optimal average performance are characterised and their performance is tested numerically.
000100902 6531_ $$athresholding
000100902 6531_ $$aaverage performance
000100902 6531_ $$aaverage sensing dictionary
000100902 6531_ $$aLTS2
000100902 6531_ $$alts2
000100902 6531_ $$aITS
000100902 6531_ $$aits
000100902 6531_ $$alts2
000100902 700__ $$0240457$$g168927$$aSchnass, Karin
000100902 700__ $$0240428$$g120906$$aVandergheynst, Pierre
000100902 773__ $$j14$$tIEEE Signal Processing Letters$$k11$$q828-831
000100902 8564_ $$uhttps://infoscience.epfl.ch/record/100902/files/AverageThresh.pdf$$zn/a$$s98635
000100902 909C0 $$xU10380$$0252392$$pLTS2
000100902 909CO $$ooai:infoscience.tind.io:100902$$qGLOBAL_SET$$pSTI$$particle
000100902 937__ $$aEPFL-ARTICLE-100902
000100902 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000100902 980__ $$aARTICLE