A descent method for least absolute deviation lasso problems

被引：1
作者：
Yue Shi
Zhiguo Feng
Ka Fai Cedric Yiu
机构：
[1] The Hong Kong Polytechnic University,Department of Applied Mathematics
[2] Chongqing Normal University,Department of Applied mathematics
[3] Ministry of Education,Key Laboratory of Optimization and Control
来源：
Optimization Letters | 2019年 / 13卷
关键词：
Least absolute deviation; LASSO; Nonsmooth optimization; Descent method;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Variable selection is an important method to analyze large quantity of data and extract useful information. Although least square regression is the most widely used scheme for its flexibility in obtaining explicit solutions, least absolute deviation (LAD) regression combined with lasso penalty becomes popular for its resistance to heavy-tailed errors in response variable, denoted as LAD-LASSO. In this paper, we consider the LAD-LASSO problem for variable selection. Based on a dynamic optimality condition of nonsmooth optimization problem, we develop a descent method to solve the nonsmooth optimization problem. Numerical experiments are conducted to confirm that the proposed method is more efficient than existing methods.
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页码：543 / 559
页数：16
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