Kernel-Optimized Based Machine for Image Recognition

Kernel learning is an important research topic in the machine learning area. Research on self-optimization learning of kernel function and its parameter has an important theoretical value for solving the kernel selection problem widely endured by kernel learning machine, and has the same important practical meaning for the improving of kernel learning systems. In this paper, we focus on two schemes: kernel optimization algorithm and procedure, the framework of kernel self-optimization learning. Finally, the proposed kernel optimization is applied into popular kernel learning methods including KPCA, KDA and KLPP. Simulation results demonstrate that the kernel self-optimization is feasible to improve various kernel-based learning methods.