Large-scale robust regression with truncated loss via majorization-minimization algorithm

The utilization of regression methods employing truncated loss functions is widely praised for its robustness in handling outliers and representing the solution in the sparse form of the samples. However, due to the non-convexity of the truncated loss, the commonly used algorithms such as difference...

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Veröffentlicht in:	European journal of operational research Jg. 319; H. 2; S. 494 - 504
Hauptverfasser:	Huang, Ling-Wei, Shao, Yuan-Hai, Lv, Xiao-Jing, Li, Chun-Na
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.12.2024
Schlagworte:	Huber regression and support vector regression Majorization-minimization algorithm Robust regression Sparsity and scalability Truncated loss function Sparsity and scalability Huber regression and support vector regression Truncated loss function Robust regression Majorization-minimization algorithm
ISSN:	0377-2217
Online-Zugang:	Volltext
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