Training support vector data descriptors using converging linear particle swarm optimization

Authors:
Hongbo Wang;Guangzhou Zhao;Nan Li
Affiliations:
College of Electrical Engineering, University of Zhejiang, Hangzhou, Zhejiang Province, China;College of Electrical Engineering, University of Zhejiang, Hangzhou, Zhejiang Province, China;College of Electrical Engineering, University of Zhejiang, Hangzhou, Zhejiang Province, China
Venue:
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part I
Year:
2010

Citing 6
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Abstract

It is known that Support Vector Domain Description (SVDD) has been introduced to detect novel data or outliers. The key problem of training a SVDD is how to solve constrained quadratic programming (QP) problem. The Linear Particle Swarm Optimization (LPSO) is developed to optimize linear constrained functions, which is intuitive and simple to implement. However, premature convergence is possible with the LPSO. The LPSO is extended to the Converging Liner PSO (CLPSO), which is guaranteed to always find at least a local optimum. A new method using CLPSO to train SVDD is proposed. Experimental results demonstrate that the proposed method is feasible and effective for SVDD training, and its performance is better than traditional method.