SVM-SVDD: a new method to solve data description problem with negative examples

Authors:
Zhigang Wang;Zeng-Shun Zhao;Changshui Zhang
Affiliations:
Department of Automation, State Key Lab of Intelligent Technologies and Systems, Tsinghua National Laboratory for Information Science and Technology(TNList), Tsinghua University, Beijing, P.R. Chi ...;College of Information and Electrical Engineering, Shandong University of Science and Technology, Qingdao, P.R. China;Department of Automation, State Key Lab of Intelligent Technologies and Systems, Tsinghua National Laboratory for Information Science and Technology(TNList), Tsinghua University, Beijing, P.R. Chi ...
Venue:
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
Year:
2013

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Abstract

Support Vector Data Description(SVDD) is an important method to solve data description or one-class classification problem. In original data description problem, only positive examples are provided in training. The performance of SVDD can be improved when a few negative examples are available which is known as SVDD_neg. Intuitively, these negative examples should cause an improvement on performance than SVDD. However, the performance of SVDD may become worse when some negative examples are available. In this paper, we propose a new approach "SVM-SVDD", in which Support Vector Machine(SVM) helps SVDD to solve data description problem with negative examples efficiently. SVM-SVDD obtains its solution by solving two convex optimization problems in two steps. We show experimentally that our method outperforms SVDD_neg in both training time and accuracy.