Distance-Based Selection of Potential Support Vectors by Kernel Matrix

We follow the idea of decomposing a large data set into smaller groups, and present a novel distance-based method of selecting potential support vectors in each group by means of kernel matrix. Potential support vectors selected in the previous group are passed to the next group for further selection. Quadratic programming is performed only once, on the potential support vectors still retained in the last group, for the construction of an optimal hyperplane. We avoid solving unnecessary quadratic programming problems at intermediate stages, and can take control over the number of selected potential support vectors to cope with the limitations of memory capacity and existing optimizers’ capability. Since this distance-based method does not work on data containing outliers and noises, we introduce the idea of separating outliers/noises and the base, by use of the k-nearest neighbor algorithm, to improve generalization ability. Two optimal hyperplanes are constructed on the base part and the outlier/noise part, respectively, which are then synthesized to derive the optimal hyperplane on the overall data.

Authors and Affiliations

1. Department of Computer Science, School of Computing, National University of Singapore, Singapore, 117543

   Baoqing Li

Editors and Affiliations

1. School of Electronic and Information Engineering, Dalian University of Technology, 116023, Dalian, China

   Fu-Liang Yin

2. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

   Jun Wang

3. School of Electronic and Information Engineering, Dalian University of Technology, 116023, Dalian, Liaoning, China

   Chengan Guo

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© 2004 Springer-Verlag Berlin Heidelberg

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