International Journal of Fuzzy Logic and Intelligent Systems

Korean Institute of Intelligent Systems (한국지능시스템학회)
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One-Class Support Vector Learning and Linear Matrix Inequalities

  • Park, Jooyoung (Dept. of Control ＆ Instrumentation Engineering, Korea University) ;
  • Kim, Jinsung (Dept. of Electrical Engineering, Korea University) ;
  • Lee, Hansung (Dept. of Computer and Information Science, Korea University) ;
  • Park, Daihee (Dept. of Computer and Information Science, Korea University)
  • Published : 2003.06.01
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Abstract

The SVDD(support vector data description) is one of the most well-known one-class support vector learning methods, in which one tries the strategy of utilizing balls defined on the kernel feature space in order to distinguish a set of normal data from all other possible abnormal objects. The major concern of this paper is to consider the problem of modifying the SVDD into the direction of utilizing ellipsoids instead of balls in order to enable better classification performance. After a brief review about the original SVDD method, this paper establishes a new method utilizing ellipsoids in feature space, and presents a solution in the form of SDP(semi-definite programming) which is an optimization problem based on linear matrix inequalities.

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