Robust support vector machines based on the rescaled hinge loss function

doi:10.1016/j.patcog.2016.09.045

	Robust support vector machines based on the rescaled hinge loss function
	Xu, Guibiao1 ; Cao, Zheng 2; Hu, Bao-Gang1 ; Principe, Jose C.2
发表期刊	PATTERN RECOGNITION
	2017-03-01
卷号	63 页码:139-148
文章类型	Article
摘要	The support vector machine (SVM) is a popular classifier in machine learning, but it is not robust to outliers. In this paper, based on the Correntropy induced loss function, we propose the resealed hinge loss function which is a monotonic, bounded and nonconvex loss that is robust to outliers. We further show that the hinge loss is a special case of the proposed resealed hinge loss. Then, we develop a new robust SVM based on the resealed hinge loss. After using the half-quadratic optimization method, we find that the new robust SVM is equivalent to an iterative weighted SVM, which can help explain the robustness of iterative weighted SVM from a loss function perspective. Experimental results confirm that the new robust SVM not only performs better than SVM and the existing robust SVMs on the datasets that have outliers, but also presents better sparseness than SVM.
关键词	Support Vector Machine Robustness Rescaled Hinge Loss Half-quadratic Optimization
WOS标题词	Science & Technology ; Technology
DOI	10.1016/j.patcog.2016.09.045
关键词[WOS]	CLASSIFICATION ; MINIMIZATION ; RECOGNITION ; CORRENTROPY ; SIGNAL
收录类别	SCI
语种	英语
项目资助者	NSFC(61273196) ; China Scholarship Council
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000389785900011
引用统计	被引频次：71[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/13367
专题	多模态人工智能系统全国重点实验室_多媒体计算
作者单位	1.Chinese Acad Sci, Inst Automat, NLPR, Beijing 100190, Peoples R China 2.Univ Florida, CNEL, Dept Elect & Comp Engn, Gainesville, FL 32611 USA
第一作者单位	模式识别国家重点实验室
推荐引用方式 GB/T 7714	Xu, Guibiao,Cao, Zheng,Hu, Bao-Gang,et al. Robust support vector machines based on the rescaled hinge loss function[J]. PATTERN RECOGNITION,2017,63:139-148.
APA	Xu, Guibiao,Cao, Zheng,Hu, Bao-Gang,&Principe, Jose C..(2017).Robust support vector machines based on the rescaled hinge loss function.PATTERN RECOGNITION,63,139-148.
MLA	Xu, Guibiao,et al."Robust support vector machines based on the rescaled hinge loss function".PATTERN RECOGNITION 63(2017):139-148.