Kernel Parameter Optimization for Kernel-based LDA methods

Kernel approach has been employed to solve classification problem with complex distribution by mapping the input space to higher dimensional feature space. However, one of the crucial factors in the Kernel approach is the choosing of kernel parameters which highly affect the performance and stability of the kernel-based learning methods. In view of this limitation, this paper adopts the Eigenvalue Stability Bounded Margin Maximization (ESBMM) algorithm to automatically tune the multiple kernel parameters for Kernel-based LDA methods. To demonstrate its effectiveness, the ESBMM algorithm has been extended and applied on two existing kernel-based LDA methods. Experimental results show that after applying the ESBMM algorithm the performance of these two methods are both improved.