A novel robust kernel for appearance-based learning

Robustness is one of the most critical issues in the appearance-based learning strategies. In this work, we propose a novel kernel that is robust against data corruption for various visual learning problems. By incorporating a robust rho-function to relieve the influence of outliers, the proposed kernel is shown to be robust against various types of outliers. By incorporating the proposed kernel into different kernel-based approaches, we verify the robustness of the proposed kernel on various applications, including face recognition and data visualization. Our experiments on these visual learning problems demonstrate the superior performance of the proposed kernel compared to the conventional kernels.