Rapid and Brief Communication: Fuzzy discriminant analysis with kernel methods

Authors:
Xiao-Hong Wu;Jian-Jiang Zhou
Affiliations:
College of Information Science & Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China and College of Electrical & Information Engineering, Jiangsu University, Zhen ...;College of Information Science & Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
Venue:
Pattern Recognition
Year:
2006

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Abstract

A novel fuzzy nonlinear classifier, called kernel fuzzy discriminant analysis (KFDA), is proposed to deal with linear non-separable problem. With kernel methods KFDA can perform efficient classification in kernel feature space. Through some nonlinear mapping the input data can be mapped implicitly into a high-dimensional kernel feature space where nonlinear pattern now appears linear. Different from fuzzy discriminant analysis (FDA) which is based on Euclidean distance, KFDA uses kernel-induced distance. Theoretical analysis and experimental results show that the proposed classifier compares favorably with FDA.