The Kernelized Geometrical Bisection Methods

Authors:
Xiaomao Liu;Shujuan Cao;Junbin Gao;Jun Zhang
Affiliations:
Department of Mathematics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China;Department of Mathematics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China;School of Information Technology, Charles Sturt University, Bathurst, NSW 2795, Australia;State Key Laboratory for Multi-Spectral Information Processing Technologies, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Year:
2007

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Abstract

In this paper, we developed two new classifiers: the kernelized geometrical bisection method and its extended version. The derivation of our methods is based on the so-called "kernel trick" in which samples in the input space are mapped onto almost linearly separable data in a high-dimensional feature space associated with a kernel function. A linear hyperplane can be constructed through bisecting the line connecting the nearest points between two convex hulls created by mapped samples in the feature space. Computational experiments show that the proposed algorithms are more competitive and effective than the well-known conventional methods.