Kernel-based nonlinear discriminant analysis using minimum squared errors criterion for multiclass and undersampled problems

It is well known that there exist two fundamental limitations in the linear discriminant analysis (LDA). One is that it cannot be applied when the within-class scatter matrix is singular, which is caused by the undersampled problem. The other is that it lacks the capability to capture the nonlinearly clustered structure of the data due to its linear nature. In this paper, a new kernel-based nonlinear discriminant analysis algorithm using minimum squared errors criterion (KDA-MSE) is proposed to solve these two problems. After mapping the original data into a higher-dimensional feature space using kernel function, the MSE criterion is used as the discriminant rule and the corresponding dimension reducing transformation is derived. Since the MSE solution does not require the scatter matrices to be nonsingular, the proposed KDA-MSE algorithm is applicable to the undersampled problem. Moreover, the new KDA-MSE algorithm can be applied to multiclass problem, whereas the existing MSE-based kernel discriminant methods are limited to handle twoclass data only. Extensive experiments, including object recognition and face recognition on three benchmark databases, are performed and the results demonstrate that our algorithm is competitive in comparison with other kernel-based discriminant techniques in terms of recognition accuracy.