A comparison of typical l(p) minimization algorithms

doi:10.1016/j.neucom.2013.03.017

CORC > 北京大学 > 信息科学技术学院

	A comparison of typical l(p) minimization algorithms
	Lyu, Qin ; Lin, Zhouchen ; She, Yiyuan ; Zhang, Chao
刊名	neurocomputing
	2013
关键词	Compressed sensing Sparse representation l(1) minimization l(p) minimization NONCONCAVE PENALIZED LIKELIHOOD SPARSE VECTOR RECONSTRUCTION GENERALIZED LINEAR-MODELS VARIABLE SELECTION L(Q) MINIMIZATION ORACLE PROPERTIES FACE RECOGNITION INVERSE PROBLEMS SIGNAL RECOVERY REGULARIZATION
DOI	10.1016/j.neucom.2013.03.017
英文摘要	Recently, compressed sensing has been widely applied to various areas such as signal processing, machine learning, and pattern recognition. To find the sparse representation of a vector w.r.t a dictionary, an l(1) minimization problem, which is convex, is usually solved in order to overcome the computational difficulty. However, to guarantee that the l(1) minimizer is close to the sparsest solution, strong incoherence conditions should be imposed. In comparison, nonconvex minimization problems such as those with the l(p) (0 < p < 1) penalties require much weaker incoherence conditions and smaller signal to noise ratio to guarantee a successful recovery. Hence the l(p) (0 < p < 1) regularization serves as a better alternative to the popular l(1) one. In this paper, we review some typical algorithms, Iteratively Reweighted l(1) minimization (IRL1), Iteratively Reweighted Least Squares (IRLS) (and its general form General Iteratively Reweighted Least Squares (GIRLS)), and Iteratively Thresholding Method (ITM), for l(p) minimization and do comprehensive comparison among them, in which IRLS is identified as having the best performance and being the fastest as well. (C) 2013 Elsevier B.V. All rights reserved.; http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000323851800046&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701 ; Computer Science, Artificial Intelligence; SCI(E); 18; ARTICLE; ,SI; 413-424; 119
语种	英语
内容类型	期刊论文
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/220145]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Lyu, Qin,Lin, Zhouchen,She, Yiyuan,et al. A comparison of typical l(p) minimization algorithms[J]. neurocomputing,2013.
APA	Lyu, Qin,Lin, Zhouchen,She, Yiyuan,&Zhang, Chao.(2013).A comparison of typical l(p) minimization algorithms.neurocomputing.
MLA	Lyu, Qin,et al."A comparison of typical l(p) minimization algorithms".neurocomputing (2013).