Robust Novelty Detection via Worst Case CVaR Minimization

doi:10.1109/TNNLS.2014.2378270

CSpace > 系统科学研究所

	Robust Novelty Detection via Worst Case CVaR Minimization
	Wang, Yongqiao 1; Dang, Chuangyin 2; Wang, Shouyang3
	2015-09-01
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN	2162-237X
卷号	26 期号:9 页码:2098-2110
摘要	Novelty detection models aim to find the minimum volume set covering a given probability mass. This paper proposes a robust single-class support vector machine (SSVM) for novelty detection, which is mainly based on the worst case conditional value-at-risk minimization. By assuming that every input is subject to an uncertainty with a specified symmetric support, this robust formulation results in a maximization term that is similar to the regularization term in the classical SSVM. When the uncertainty set is 1-norm, 00-norm or box, its training can be reformulated to a linear program; while the uncertainty set is 2-norm or ellipsoidal, its training is a tractable secondorder cone program. The proposed method has a nice consistent statistical property. As the training size goes to infinity, the estimated normal region converges to the true provided that the magnitude of the uncertainty set decreases in a systematic way. The experimental results on three data sets clearly demonstrate its superiority over three benchmark models.
关键词	Conditional value-at-risk (CVaR) kernel methods novelty detection robust programming single-class support vector machine (SSVM)
DOI	10.1109/TNNLS.2014.2378270
语种	英语
资助项目	National Natural Science Foundation of China[71101127] ; Social Sciences Foundation through the Ministry of Education, China[10YJC790265] ; Zhejiang Province Universities Social Sciences Key Base through the Finance Research Center, Zhejiang Gongshang University, Hangzhou, China ; Hong Kong Government[CityU 112910]
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:000360437300020
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/20680
专题	系统科学研究所
通讯作者	Wang, Yongqiao
作者单位	1.Zhejiang Gongshang Univ, Sch Finance, Hangzhou 310018, Zhejiang, Peoples R China 2.City Univ Hong Kong, Dept Syst Engn & Engn Management, Hong Kong, Hong Kong, Peoples R China 3.Chinese Acad Sci, Inst Syst Sci, Acad Math & Syst Sci, Beijing 100080, Peoples R China
推荐引用方式 GB/T 7714	Wang, Yongqiao,Dang, Chuangyin,Wang, Shouyang. Robust Novelty Detection via Worst Case CVaR Minimization[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2015,26(9):2098-2110.
APA	Wang, Yongqiao,Dang, Chuangyin,&Wang, Shouyang.(2015).Robust Novelty Detection via Worst Case CVaR Minimization.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,26(9),2098-2110.
MLA	Wang, Yongqiao,et al."Robust Novelty Detection via Worst Case CVaR Minimization".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 26.9(2015):2098-2110.