Large margin nonlinear discriminant analysis of minimization within class scatter
Xiuhong Chen · Computer Engineering and Applications Journal · 2010
Discriminant analysis is one of crucial issues for the statistic-based face recognition method.This paper proposes a novel large margin nonlinear discriminant analysis of minimization within class scatter.The underlying idea is that the kernel trick is used firstly to project the original samples into an implicit space called feature space by nonlinear kernel mapping,the kernel trick is used to improve the traditional large margin classifier algorithm,moreover,the theory of reproducing kernel in the new feature space is used to obtain the optimal kernel discriminant vectors with which the kernel within scatter is kept as small as possible.Finally,the proposed method is tested on ORL face database,the results prove this arithmetic optimal.