Efficient algorithm for kernel discriminant analysis

An efficient algorithm for kernel discriminant analysis is developed, which applies the maximal Fisher criterion value and the minimal statistical correlation between feature vectors. In some sense, the proposed algorithm is a generalisation of Xu´s method as a nonlinear feature extraction method. Experiments on ORL face database demonstrate that the proposed algorithm is effective.