Sparse Recovery by Means of Nonnegative Least Squares

被引：46
|
作者：
Foucart, Simon [1 ]
Koslicki, David [2 ]
机构：
[1] Univ Georgia, Dept Math, Athens, GA 30602 USA
[2] Oregon State Univ, Dept Math, Corvallis, OR 97331 USA
来源：
IEEE SIGNAL PROCESSING LETTERS | 2014年 / 21卷 / 04期
基金：
美国国家科学基金会;
关键词：
Adjacency matrices of bipartite graphs; compressive sensing; Gaussian matrices; k-mer frequency matrices; l(1)-minimization; nonnegative least squares; orthogonal matching pursuit; sparse recovery; RECONSTRUCTION;
D O I：
10.1109/LSP.2014.2307064
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：
This letter demonstrates that sparse recovery can be achieved by an l(1)-minimization ersatz easily implemented using a conventional nonnegative least squares algorithm. A connection with orthogonal matching pursuit is also highlighted. The preliminary results call for more investigations on the potential of the method and on its relations to classical sparse recovery algorithms.
引用
收藏
页码：498 / 502
页数：5
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