Adaptive Training of a Kernel-Based Representative and Discriminative Nonlinear Classifier

Authors:
Benyong Liu;Jing Zhang;Xiaowei Chen
Affiliations:
College of Computer Science and Technology, Guizhou University, Huaxi 550025, Guiyang, China;College of Computer Science and Technology, Guizhou University, Huaxi 550025, Guiyang, China;College of Computer Science and Technology, Guizhou University, Huaxi 550025, Guiyang, 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

Adaptive training of a classifier is necessary when feature selection and sparse representation are considered. Previously, we proposed a kernel-based nonlinear classifier for simultaneous representation and discrimination of pattern features. Its batch training has a closed-form solution. In this paper we implement an adaptive training algorithm using an incremental learning procedure that exactly retains the generalization ability of batch training. It naturally yields a sparse representation. The feasibility of the presented methods is illustrated by experimental results on handwritten digit classification.