Fast kernel feature ranking using class separability for big data mining

Kernel feature ranking often delivers many benefits for big data mining, e.g., improving generalization performance. However, its efficiency is quite challenging due to a need of tuning kernel parameters in the ranking process. In present work, we propose a computational-light metric based on kernel class separability for kernel feature ranking. In the proposed metric, the kernel parameter is optimized by a proposed analytical algorithm rather than an optimization search algorithm. Experimental results demonstrate that (1) the proposed metric can lead to a fast and robust kernel feature ranking; and (2) the proposed analytical algorithm can select a right kernel parameter with much less computation time for two state-of-the-arts kernel metrics.