Low-rank elastic-net regularized multivariate Huber regression model

Heavy-tailed noise or strongly correlated predictors often go with the multivariate linear regression model. To tackle with these problems, this paper focuses on the matrix elastic-net regularized multivariate Huber regression model. This new model possesses the grouping effect property and the robu...

Full description

Saved in:
Bibliographic Details
Published in:Applied Mathematical Modelling Vol. 87; p. 571
Main Authors: Chen, Bingzhen, Zhai, Wenjuan, Huang, Zhiyong
Format: Journal Article
Language:English
Published: New York Elsevier BV 01.11.2020
Subjects:
Algorithms
Data analysis
Multivariate analysis
Noise prediction
Outliers (statistics)
Regression analysis
Regression models
Robustness (mathematics)
ISSN:1088-8691, 0307-904X
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Heavy-tailed noise or strongly correlated predictors often go with the multivariate linear regression model. To tackle with these problems, this paper focuses on the matrix elastic-net regularized multivariate Huber regression model. This new model possesses the grouping effect property and the robustness to heavy-tailed noise. Meanwhile, it also has the ability of reducing the negative effect of outliers due to Huber loss. Furthermore, an accelerated proximal gradient algorithm is designed to solve the proposed model. Some numerical studies including a real data analysis are dedicated to show the efficiency of our method.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1088-8691
0307-904X
DOI:10.1016/j.apm.2020.05.012