A LOW COMPLEXITY ORTHOGONAL MATCHING PURSUIT FOR SPARSE SIGNAL APPROXIMATION WITH SHIFT-INVARIANT DICTIONARIES

被引：34

作者：

Mailhe, Boris ^{[1
]}

Gribonval, Remi ^{[1
]}

Bimbot, Frederic ^{[1
]}

Vandergheynst, Pierre ^{[2
]}

机构：

[1] IRISA, Ctr Rech INRIA Rennes Bretagne Atlantique, Campus Beaulieu, F-35042 Rennes, France

[2] Ecole Polytech Fed Lausanne, Sch Engn, Signal Proc Labs LTS, CH-1015 Lausanne, Switzerland

来源：

2009 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS 1- 8, PROCEEDINGS | 2009年

关键词：

sparse approximation; greedy algorithms; shift-invariance; orthogonal matching pursuit;

D O I：

10.1109/ICASSP.2009.4960366

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

We 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.

引用

页码：3445 / +

页数：2

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