Kernel-distance target alignment

Abstract

The success of kernel methods are dependent on the kernel, thus a choice of a kernel and proper setting of its parameters are crucial importance. Learning a kernel from the data requires evaluation measures to assess the quality of the kernel. In this paper, we propose a new measure named kernel distance target alignment (KDTA). The measure retains the property of state-of-the-art evaluation measures, kernel target alignment (KTA) and feature space-based kernel matrix evaluation measure (FSM), additionally overcomes the limitation of them. Comparative experiments indicate that the new measure is a good indication of the superiority of a kernel and can get better parameter of RBF kernel.