A Criterion for Learning the Data-Dependent Kernel for Classification

Authors:
Jun-Bao Li;Shu-Chuan Chu;Jeng-Shyang Pan
Affiliations:
Department of Automatic Test and Control, Harbin Institute of Technology, Harbin, China;Department of Information Management, Cheng Shiu University, Kaohsiung, Taiwan;Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan
Venue:
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
Year:
2007

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Abstract

A novel criterion, namely Maximum Margin Criterion (MMC), is proposed for learning the data-dependent kernel for classification. Different kernels create the different geometrical structures of the data in the feature space, and lead to different class discrimination. Selection of kernel influences greatly the performance of kernel learning. Optimizing kernel is an effective method to improve the classification performance. In this paper, we propose a novel kernel optimization method based on maximum margin criterion, which can solve the problem of Xiong's work [1] that the optimal solution can be solved by iteration update algorithm owing to the singular problem of matrix. Our method can obtain a unique optimal solution by solving an eigenvalue problem, and the performance is enhanced while time consuming is decreased. Experimental results show that the proposed algorithm gives a better performance and a lower time consuming compared with Xiong's work.