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...

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Veröffentlicht in:Applied Mathematical Modelling Jg. 87; S. 571
Hauptverfasser: Chen, Bingzhen, Zhai, Wenjuan, Huang, Zhiyong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Elsevier BV 01.11.2020
Schlagworte:
Algorithms
Data analysis
Multivariate analysis
Noise prediction
Outliers (statistics)
Regression analysis
Regression models
Robustness (mathematics)
ISSN:1088-8691, 0307-904X
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:1088-8691
0307-904X
DOI:10.1016/j.apm.2020.05.012