A Regularized Nonlinear Discrimination Approach

For nonlinear discrimination analysis technique, there are some key points worthy of further research. One is finding an effective rule to select appropriate kernel function parameter for different sample sets. Another is providing a simple and efficient solution for the singularity problem of within-class scatter matrix. In this paper, we focus on these two points and address a regularized nonlinear discrimination analysis approach. We first present a definition of regularized within-class scatter and provide a very simple solution of regularization parameter. Then, a nonlinear discriminant judgment is proposed to select the parameter of radial basis function. A large public face database is used as the test data. The experimental results demonstrate that the proposed approach outperforms several representative nonlinear discrimination methods.