Efficient kernel selection via spectral analysis
Published in In Procedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI 2017), 2017
Jian Li, Yong Liu, Hailun Lin, Yinliang Yue, Weiping Wang. In Procedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI 2017) .
Abstract
Kernel selection is a fundamental problem of kernel methods. Existing measures for kernel selection either provide less theoretical guarantee or have high computational complexity. In this paper, we propose a novel kernel selection criterion based on a newly defined spectral measure of a kernel matrix, with sound theoretical foundation and high computational efficiency. We first show that the spectral measure can be used to derive generalization bounds for some kernel-based algorithms. By minimizing the derived generalization bounds, we propose the kernel selection criterion with spectral measure. Moreover, we demonstrate that the popular minimum graph cut and maximum mean discrepancy are two special cases of the proposed criterion. Experimental results on lots of data sets show that our proposed criterion can not only give the comparable results as the state-of-the-art criterion, but also significantly improve the efficiency.