Matrix pattern based minimum within-class scatter support vector machines

Authors:
Gao Jun;Fu-lai Chung;Shitong Wang
Affiliations:
School of Information, JiangNan University, WuXi, JiangSu, China and The key lab. of information technologies at Suzhou University, Jiangsu Province, China;Dept. Computing, HongKong Polytechnic University, Hong Kong, China;School of Information, JiangNan University, WuXi, JiangSu, China and Dept. Computing, HongKong Polytechnic University, Hong Kong, China
Venue:
Applied Soft Computing
Year:
2011

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Abstract

Based on minimum within-class scatter support vector machines (MCSVM), a new matrix pattern based MCSSVM (MCSVM^m^a^t^r^i^x) is presented. Accordingly, it is extended by introducing Mercer's kernels in order to solve the problem of nonlinear decision boundaries, which presents a significant matrix pattern based nonlinear support vector machines: Ker-MCSVM^m^a^t^r^i^x. The above-mentioned approaches not only keep the merits of MCSVM, but, owing to introducing matrix pattern based within-class scatter matrix into support vector machines, theoretically better solve the singular problem of within-class scatter matrix when small sample size problems are dealt with, reduce the time/place complexity when within-class scatter matrix, its invertible matrix and weight vector @w are calculated. Hence, the classification accuracy is improved to certain extent. Experimental results indicate the above advantages of the proposed methods: both MCSVM^m^a^t^r^i^x and Ker-MCSVM^m^a^t^r^i^x.