Properties and iterative methods for the lasso and its variants

被引：49
作者：
Xu, Hong-Kun [1 ,2 ]
机构：
[1] Natl Sun Yat Sen Univ, Dept Appl Math, Kaohsiung 80424, Taiwan
[2] King Abdulaziz Univ, Dept Math, Jeddah 21589, Saudi Arabia
来源：
CHINESE ANNALS OF MATHEMATICS SERIES B | 2014年 / 35卷 / 03期
关键词：
Lasso; Elastic net; Smooth-lasso; l(1) regularization; Sparsity; Proximal method; Dual method; Projection; Thresholding; SIGNAL RECOVERY; SELECTION; REGRESSION;
D O I：
10.1007/s11401-014-0829-9
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
The lasso of Tibshirani (1996) is a least-squares problem regularized by the a"" (1) norm. Due to the sparseness promoting property of the a"" (1) norm, the lasso has been received much attention in recent years. In this paper some basic properties of the lasso and two variants of it are exploited. Moreover, the proximal method and its variants such as the relaxed proximal algorithm and a dual method for solving the lasso by iterative algorithms are presented.
引用
收藏
页码：501 / 518
页数：18
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