A novel robust kernel for visual learning problems

A major challenge to appearance-based learning techniques is the robustness against data corruption and irrelevant within-class data variation. This paper presents a robust kernel for kernel-based approach to achieving better robustness on several visual learning problems. Incorporating a robust error function used in robust statistics together with a deformation invariant distance measure, the proposed kernel is shown to be insensitive to noise and robust to intra-class variations. We prove that this robust kernel satisfies the requirements for a valid kernel, so it has good properties when used with kernel-based learning machines. In the experiments, we validate the superior robustness of the proposed kernel over the state-of-the-art algorithms on several applications, including hand-written digit classification, face recognition and data visualization.