Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/74472
Title: A descent method for least absolute deviation lasso problems
Authors: Shi, Y 
Feng, Z 
Yiu, KFC 
Keywords: Descent method
LASSO
Least absolute deviation
Nonsmooth optimization
Issue Date: 2017
Publisher: Springer
Source: Optimization letters, 2017, p. 1-17 How to cite?
Journal: Optimization letters 
Abstract: 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.
URI: http://hdl.handle.net/10397/74472
ISSN: 1862-4472
EISSN: 1862-4480
DOI: 10.1007/s11590-017-1157-2
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

Page view(s)

163
Last Week
6
Last month
Citations as of May 6, 2020

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.