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__ $$a2009
000131255 260__ $$aTaipei, Taiwan$$c2009
000131255 336__ $$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__ $$0240428$$aVandergheynst, Pierre$$g120906
000131255 7112_ $$aIEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP09)$$cTaipei, Taiwan$$d2009
000131255 8564_ $$s319783$$uhttps://infoscience.epfl.ch/record/131255/files/LocOMP.pdf$$zn/a
000131255 909C0 $$0252392$$pLTS2$$xU10380
000131255 909CO $$ooai:infoscience.tind.io:131255$$pconf$$pSTI$$qGLOBAL_SET
000131255 917Z8 $$x120906
000131255 937__ $$aEPFL-CONF-131255
000131255 973__ $$aEPFL$$rREVIEWED$$sPUBLISHED
000131255 980__ $$aCONF