Robust C-Loss Kernel Classifiers

doi:10.1109/TNNLS.2016.2637351

	Robust C-Loss Kernel Classifiers
	Xu, Guibiao1 ; Hu, Bao-Gang1 ; Principe, Jose C.2
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
	2018-03-01
卷号	29 期号:3 页码:510-522
文章类型	Article
摘要	The correntropy-induced loss (C-loss) function has the nice property of being robust to outliers. In this paper, we study the C-loss kernel classifier with the Tikhonov regularization term, which is used to avoid overfitting. After using the half-quadratic optimization algorithm, which converges much faster than the gradient optimization algorithm, we find out that the resulting C-loss kernel classifier is equivalent to an iterative weighted least square support vector machine (LS-SVM). This relationship helps explain the robustness of iterative weighted LS-SVM from the correntropy and density estimation perspectives. On the large-scale data sets which have low-rank Gram matrices, we suggest to use incomplete Cholesky decomposition to speed up the training process. Moreover, we use the representer theorem to improve the sparseness of the resulting C-loss kernel classifier. Experimental results confirm that our methods are more robust to outliers than the existing common classifiers.
关键词	Correntropy Half-quadratic (Hq) Optimization Kernel Classifier Loss Function
WOS标题词	Science & Technology ; Technology
DOI	10.1109/TNNLS.2016.2637351
关键词[WOS]	SUPPORT VECTOR MACHINES ; LABEL NOISE ; DATA SETS ; CLASSIFICATION ; CORRENTROPY ; ALGORITHMS ; MINIMIZATION ; REGRESSION ; FRAMEWORK ; SIGNAL
收录类别	SCI
语种	英语
项目资助者	NSFC(61273196 ; China Scholarship Council within Computational NeuroEngineering Laboratory ; 61573348)
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:000426344600001
引用统计	被引频次：28[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/173211/19986
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, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Univ Florida, Dept Elect & Comp Engn, Computat NeuroEngn Lab, 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,Hu, Bao-Gang,Principe, Jose C.. Robust C-Loss Kernel Classifiers[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2018,29(3):510-522.
APA	Xu, Guibiao,Hu, Bao-Gang,&Principe, Jose C..(2018).Robust C-Loss Kernel Classifiers.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,29(3),510-522.
MLA	Xu, Guibiao,et al."Robust C-Loss Kernel Classifiers".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 29.3(2018):510-522.

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