Extraction of Discriminant Features Based on Optimal Transformation and Cluster Centers of Kernel Space

As is known, kernel method is one of the most important research points in statistical learning theory, and the kernel-based learning methods are attracting extensive research interests. It has been proved that a lot of linear feature extraction methods can be generalized to the nonlinear learning methods by using kernel methods. In this paper, a new nonlinear learning method of optimal transformation and cluster centers (OT-CC) is presented by using kernel technique. Data are mapped into the high dimensional kernel space via nonlinear transformation from original space, and then the learning method of optimal transformation and cluster centers is applied for feature extraction. However, the kernel function is utilized in result resolving process so that the complex expression of nonlinear transformation is avoided. The novel method is named optimal transformation and cluster center algorithm of kernel space (KOT-CC), which is a powerful technique for extracting nonlinear discriminant features and is very effective in solving pattern recognition problems where the overlap between patterns is serious. A large number of experiments demonstrate that the new algorithm outperforms OT-CC and kernel Fisher discriminant analysis (KFDA) in ability for extracting nonlinear discriminant features and computation complexity. Furthermore, the problem of “course dimensionality” is avoided.