CSpace
Robust Novelty Detection via Worst Case CVaR Minimization
Wang, Yongqiao1; Dang, Chuangyin2; Wang, Shouyang3
2015-09-01
Source PublicationIEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN2162-237X
Volume26Issue:9Pages:2098-2110
AbstractNovelty 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.
KeywordConditional value-at-risk (CVaR) kernel methods novelty detection robust programming single-class support vector machine (SSVM)
DOI10.1109/TNNLS.2014.2378270
Language英语
Funding ProjectNational 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 Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000360437300020
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/20680
Collection中国科学院数学与系统科学研究院
Affiliation1.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
Recommended Citation
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.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Wang, Yongqiao]'s Articles
[Dang, Chuangyin]'s Articles
[Wang, Shouyang]'s Articles
Baidu academic
Similar articles in Baidu academic
[Wang, Yongqiao]'s Articles
[Dang, Chuangyin]'s Articles
[Wang, Shouyang]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Wang, Yongqiao]'s Articles
[Dang, Chuangyin]'s Articles
[Wang, Shouyang]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.