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CLC number: TP301

On-line Access: 2011-05-09

Received: 2010-10-15

Revision Accepted: 2011-02-23

Crosschecked: 2011-04-07

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Journal of Zhejiang University SCIENCE C 2011 Vol.12 No.5 P.362-370


Integrating outlier filtering in large margin training

Author(s):  Xi-chuan Zhou, Hai-bin Shen, Jie-ping Ye

Affiliation(s):  College of Communication Engineering, Chongqing University, Chongqing 400044, China, School of Electrical Engineering, Zhejiang University, Hangzhou 310027, China, Department of Computer Science and Engineering, Arizona State University, Tempe 85281, USA

Corresponding email(s):   zxc@ccee.cqu.edu.cn

Key Words:  Support vector machines, Outlier filter, Semi-definite programming, Multi-stage relaxation

Xi-chuan Zhou, Hai-bin Shen, Jie-ping Ye. Integrating outlier filtering in large margin training[J]. Journal of Zhejiang University Science C, 2011, 12(5): 362-370.

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%T Integrating outlier filtering in large margin training
%A Xi-chuan Zhou
%A Hai-bin Shen
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%DOI 10.1631/jzus.C1000361

T1 - Integrating outlier filtering in large margin training
A1 - Xi-chuan Zhou
A1 - Hai-bin Shen
A1 - Jie-ping Ye
J0 - Journal of Zhejiang University Science C
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EP - 370
%@ 1869-1951
Y1 - 2011
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.C1000361

Large margin classifiers such as support vector machines (SVM) have been applied successfully in various classification tasks. However, their performance may be significantly degraded in the presence of outliers. In this paper, we propose a robust SVM formulation which is shown to be less sensitive to outliers. The key idea is to employ an adaptively weighted hinge loss that explicitly incorporates outlier filtering in the SVM training, thus performing outlier filtering and classification simultaneously. The resulting robust SVM formulation is non-convex. We first relax it into a semi-definite programming which admits a global solution. To improve the efficiency, an iterative approach is developed. We have performed experiments using both synthetic and real-world data. Results show that the performance of the standard SVM degrades rapidly when more outliers are included, while the proposed robust SVM training is more stable in the presence of outliers.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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