Data-Dependent Kernel Function Based Kernel Optimization Algorithm
LI Jun-Bao1,2, GAO Hui-Jun2
1.Department of Automatic Test and Control,Harbin Institute of Technology,Harbin 150001 2.Department of Control Science and Engineering,Harbin Institute of Technology,Harbin 150001
Abstract：To solve the selection problem of kernel function and its parameters in kernel learning to enhance the performance of the algorihtm, data-dependent kernel function based kernel optimization method is proposed in this paper. The optimal objective function is built through the maximum margin criterion to solve the optimal parameter of data-dependent kernel. Experimental results show that the proposed algorithm can effectively increase the performance of kernel learning machine.
[1] Wang Lei, Chan K L, Xue Ping. A Criterion for Optimizing Kernel Parameters in KBDA for Image Retrieval. IEEE Trans on Systems, Man and Cybernetics, 2005, 35(3): 556-562 [2] Huang Jian, Yuen P C, Chen Wensheng, et al. Kernel Subspace LDA with Optimized Kernel Parameters on Face Recognition // Proc of the 6th IEEE International Conference on Automatic Face and Gesture Recognition. Southampton, UK, 2004: 1352-1355 [3] Chen Wensheng, Yuen P C, Huang Jian, et al. Kernel Machine-Based One-Parameter Regularized Fisher Discriminant Method for Face Recognition. IEEE Trans on Systems, Man and Cybernetics, 2005, 35(4): 659-669 [4] Micchelli C A, Pontil M. Learning the Kernel Function via Regularization. Journal of Machine Learning Research. 2005, 6: 1099-1125 [5] Lanckriet G R G, Cristianini N, Bartlett P, et al. Learning the Kernel Matrix with Semidefinte Programming. Journal of Machine Learning Research. 2004, 5: 27-72 [6] Dai Guang, Yeung D Y. Kernel Selection for Semi-Supervised Kernel Machines // Proc of the 24th International Conference on Machine Learning. Corvalis, Oregon, 2007: 1457-1465 [7] Xiong Huilin, Swamy M N S, Ahmad M O. Optimizing the Kernel in the Empirical Feature Space. IEEE Trans on Neural Networks. 2005, 16(2): 460-474 [8] Cristianini N, Kandola J, Elisseeff A, et al. On Kernel Target Alignment // Dietterich T G, Becker S, Ghahramani Z. Advances in Neural Information Processing Systems. Cambridge, USA: MIT Press, 2002, XIV: 367-373 [9] Amari S, Wu S. Improving Support Vector Machine Classifiers by Modifying Kernel Functions. Neural Networks, 1999, 12(6): 783-789 [10] Tao Qing, Chu Dejun, Wang Jue. Recursive Support Vector Machines for Dimensionality Reduction. IEEE Trans on Neural Networks, 2008, 19(1): 189-193 [11] Li Haifeng, Jiang Tao, Zhang Keshu. Efficient and Robust Feature Extraction by Maximum Margin Criterion. IEEE Trans on Neural Networks, 2006, 17(1): 157-165 [12] Samaria F S, Harter A C. Parameterisation of a Stochastic Model for Human Face Identification // Proc of the 2nd IEEE Workshop on Applications of Computer Vision. Sarasota, USA, 1994: 138-142
2010, Vol. 23 
