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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| Published in: | Applied Mathematical Modelling Vol. 87; p. 571 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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New York
Elsevier BV
01.11.2020
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| ISSN: | 1088-8691, 0307-904X |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Zhai, Wenjuan Huang, Zhiyong Chen, Bingzhen |
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| CitedBy_id | crossref_primary_10_1080_00949655_2023_2232504 crossref_primary_10_1016_j_apm_2023_06_039 crossref_primary_10_1007_s10182_021_00403_x crossref_primary_10_1080_03610918_2025_2496769 crossref_primary_10_1109_TNNLS_2022_3189069 crossref_primary_10_1016_j_apm_2021_12_016 |
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| Snippet | Heavy-tailed noise or strongly correlated predictors often go with the multivariate linear regression model. To tackle with these problems, this paper focuses... |
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| SubjectTerms | Algorithms Data analysis Multivariate analysis Noise prediction Outliers (statistics) Regression analysis Regression models Robustness (mathematics) |
| Title | Low-rank elastic-net regularized multivariate Huber regression model |
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