000131255 001__ 131255
000131255 005__ 20190316234452.0
000131255 037__ $$aCONF
000131255 245__ $$aA low complexity orthogonal matching pursuit for sparse signal approximation with shift-invariant dictionaries
000131255 269__ $$a2009
000131255 260__ $$c2009$$aTaipei, Taiwan
000131255 336__ $$aConference Papers
000131255 520__ $$aWe propose a variant of Orthogonal Matching Pursuit (OMP), called LoCOMP, for scalable sparse signal approximation. The algorithm is designed for shift- invariant signal dictionaries with localized atoms, such as time-frequency dictionaries, and achieves approximation performance comparable to OMP at a computational cost similar to Matching Pursuit. Numerical experiments with a large audio signal show that, compared to OMP and Gradient Pursuit, the proposed algorithm runs in over 500 less time while leaving the approximation error almost unchanged.
000131255 6531_ $$asparse approximation
000131255 6531_ $$agreedy algorithms
000131255 6531_ $$ashift-invariance
000131255 6531_ $$aorthogonal matching pursuit
000131255 6531_ $$alts2
000131255 6531_ $$aLTS2
000131255 700__ $$aMailhé, Boris
000131255 700__ $$aGribonval, Rémi
000131255 700__ $$aBimbot, Frédéric
000131255 700__ $$g120906$$aVandergheynst, Pierre$$0240428
000131255 7112_ $$d2009$$cTaipei, Taiwan$$aIEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP09)
000131255 8564_ $$uhttps://infoscience.epfl.ch/record/131255/files/LocOMP.pdf$$zn/a$$s319783
000131255 909C0 $$xU10380$$0252392$$pLTS2
000131255 909CO $$qGLOBAL_SET$$pconf$$ooai:infoscience.tind.io:131255$$pSTI
000131255 917Z8 $$x120906
000131255 937__ $$aEPFL-CONF-131255
000131255 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000131255 980__ $$aCONF