CASIA OpenIR  > 模式识别国家重点实验室  > 多媒体计算与图形学
Robust support vector machines based on the rescaled hinge loss function
Xu, Guibiao1; Cao, Zheng2; Hu, Bao-Gang1; Principe, Jose C.2
Source PublicationPATTERN RECOGNITION
2017-03-01
Volume63Pages:139-148
SubtypeArticle
AbstractThe 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.
KeywordSupport Vector Machine Robustness Rescaled Hinge Loss Half-quadratic Optimization
WOS HeadingsScience & Technology ; Technology
DOI10.1016/j.patcog.2016.09.045
WOS KeywordCLASSIFICATION ; MINIMIZATION ; RECOGNITION ; CORRENTROPY ; SIGNAL
Indexed BySCI
Language英语
Funding OrganizationNSFC(61273196) ; China Scholarship Council
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000389785900011
Citation statistics
Cited Times:18[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/13367
Collection模式识别国家重点实验室_多媒体计算与图形学
Affiliation1.Chinese Acad Sci, Inst Automat, NLPR, Beijing 100190, Peoples R China
2.Univ Florida, CNEL, Dept Elect & Comp Engn, Gainesville, FL 32611 USA
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
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.
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