Robust Novelty Detection via Worst Case CVaR Minimization | |
Wang, Yongqiao1; Dang, Chuangyin2; Wang, Shouyang3 | |
刊名 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS |
2015-09-01 | |
卷号 | 26期号:9页码:2098-2110 |
关键词 | Conditional value-at-risk (CVaR) kernel methods novelty detection robust programming single-class support vector machine (SSVM) |
ISSN号 | 2162-237X |
DOI | 10.1109/TNNLS.2014.2378270 |
英文摘要 | 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. |
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 |
语种 | 英语 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
WOS记录号 | WOS:000360437300020 |
内容类型 | 期刊论文 |
源URL | [http://ir.amss.ac.cn/handle/2S8OKBNM/20679] |
专题 | 中国科学院数学与系统科学研究院 |
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. |
个性服务 |
查看访问统计 |
相关权益政策 |
暂无数据 |
收藏/分享 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论