L1/2 regularization

被引:2
|
作者
XU ZongBen 1
2 Department of Mathematics
3 University of Science and Technology
机构
基金
中国国家自然科学基金;
关键词
machine learning; variable selection; regularizer; compressed sensing;
D O I
暂无
中图分类号
TP181 [自动推理、机器学习];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this paper we propose an L1 /2 regularizer which has a nonconvex penalty.The L 1/2 regularizer is shown to have many promising properties such as unbiasedness, sparsity and oracle properties.A reweighed iterative algorithm is proposed so that the solution of the L 1/2 regularizer can be solved through transforming it into the solution of a series of L 1 regularizers.The solution of the L 1/2 regularizer is more sparse than that of the L 1 regularizer, while solving the L 1/2 regularizer is much simpler than solving the L 0 regularizer.The experiments show that the L 1/2 regularizer is very useful and efficient, and can be taken as a representative of the Lp(0 < p < 1) regularizer.
引用
收藏
页码:1159 / 1169
页数:11
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