A Fast Approach for Overcomplete Sparse Decomposition Based on Smoothed l0 Norm

被引：892

作者：

Mohimani, Hosein ^{[1
]}

Babaie-Zadeh, Massoud ^{[1
]}

Jutten, Christian ^{[2
]}

机构：

[1] Sharif Univ Technol, Dept Elect Engn, Tehran 145889694, Iran

[2] Inst Natl Polytech Grenoble, Dept Images & Signals, GIPSA Lab, F-38031 Grenoble, France

来源：

IEEE TRANSACTIONS ON SIGNAL PROCESSING | 2009年 / 57卷 / 01期

基金：

美国国家科学基金会;

关键词：

Atomic decomposition; blind source separation (BSS); compressed sensing; overcomplete signal representation; sparse component analysis (SCA); sparse decomposition; sparse source separation; BLIND SOURCE SEPARATION; SIGNAL RECONSTRUCTION; REPRESENTATION; ALGORITHM;

D O I：

10.1109/TSP.2008.2007606

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this paper, a fast algorithm for overcomplete sparse decomposition, called SL0, is proposed. The algorithm is essentially a method for obtaining sparse solutions of underdetermined systems of linear equations, and its applications include underdetermined sparse component analysis (SCA), atomic decomposition on overcomplete dictionaries, compressed sensing, and decoding real field codes. Contrary to previous methods, which usually solve this problem by minimizing the l(1) norm using linear programming (LP) techniques, our algorithm tries to directly minimize the l(0) norm. It is experimentally shown that the proposed algorithm is about two to three orders of magnitude faster than the state-of-the-art interior-point LP solvers, while providing the same (or better) accuracy.

引用

页码：289 / 301

页数：13

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