Schlkopf B, Smola A, Muller K R. Nonlinear Component Analysis as a Kernel Eigenvalue Problem. Neural Computation, 1998, 10(5): 1299-1319
[2]
Mika S, Ratsch G, Weston J, et al. Fisher Discriminant Analysis with Kernels // Proc of the IEEE Signal Processing Society Workshop on Neural Networks for Signal Processing. Madison, USA, 1999, IX: 41-48
[3]
Williams C K I, Seeger M. Using the Nystrm Method to Speed up Kernel Machines // Leen T K, Dietterich T G, Tresp V, eds. Advances in Neural Information Processing Systems. Cambridge, USA: MIT Press, 2000, XIII: 682-688
[4]
Teixeira A R, Tomé A M, Lang E W. Feature Extraction Using Low-Rank Approximations of the Kernel Matrix // Proc of the 5th International Conference on Image Analysis and Recognition. Póvoa de Varzim, Portugal, 2008: 404-412
[5]
Drineas P, Kannan R, Mahoney M W. Fast Monte Carlo Algorithm for Matrices II: Computing a Low-Rank Approximation to a Matrix. SIAM Journal of Computing, 2006, 36(1): 158-183
[6]
Drineas P, Mahoney M W. On the Nystrm Method for Approximating a Gram Matrix for Improved Kernel-Based Learning. Journal of Machine Learning Research, 2005, 6: 2153-2175
[7]
Belabbas M A, Wolfe P J. Spectral Methods in Machine Learning and New Strategies for Very Large Data Sets. Proc of the National Academy of Science, 2009, 106(2): 369-374
[8]
Boutsidis C, Mahoney M W, Drineas P. An Improved Approximation Algorithm for the Column Subset Problem // Proc of the 20th Annual ACM-SIAM Symposium on Discrete Algorithm. New York, USA, 2009: 968-977
[9]
Ye Qiaolin, Ye Ning, Zhang Xunhua. Extremum Decomposition Based Mixtures of Kernels and Its Improvement. Pattern Recognition and Artificial Intelligence, 2009, 22(3): 366-373 (in Chinese)(业巧林,业 宁,张训华.基于极分解下的混合核函数及改进.模式识别与人工智能, 2009, 22(3): 366-373)
[10]
Smola A J, Schlkopf B. Sparse Greedy Matrix Approximation for Machine Learning // Proc of the 17th International Conference on Machine Learning. Stanford, USA, 2000: 911-918
[11]
Fine S, Scheinberg K. Efficient SVM Training Using Low-Rank Kernel Representation. Journal of Machine Learning Research, 2001, 2: 243-264
[12]
Lanckrite G R G, Cristianini N, Bartlett P, et al. Learning the Kernel Matrix with Semidefinite Programming. Journal of Machine Learning Research, 2004, 5: 27-72
[13]
Bach F R, Jordan M I.Predictive Low-Rank Decomposition for Kernel Methods // Proc of the 22nd International Conference on Machine Learning. Bonn, Germany, 2005: 33-40
[14]
Kulis B, Sustik M A, Dhillon I S. Low-Rank Kernel Learning with Bregman Matrix Divergences. Journal of Machine Learning Research, 2009,10: 341-376
[15]
Peng H C, Long F H, Ding C. Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy. IEEE Trans on Pattern Analysis and Machine Intelligence, 2005, 27(8): 1226-1238
[16]
Estévez P A, Tesmer M, Perez C A, et al. Normalized Mutual Information Feature Selection. IEEE Trans on Neural Networks, 2009, 20(2): 189-201
[17]
Torkkola K. Feature Extraction by Non-Parametric Mutual Information Maximization. Journal of Machine Learning Research, 2003, 3: 1415-1438
[18]
Botev Z I, Grotowski J F, Kroese D P. Kernel Density Estimation via Diffusion. Annals of Statistics, 2010, 38(5): 2916-2957
[19]
Golub G H, van Loan C F. Matrix Computation. 3rd Edition. Baltimore, USA: The Johns Hopkins University Press, 1996
[20]
Fowlkes C, Belongie S, Chung F, et al. Spectral Grouping Using the Nystrm Method. IEEE Trans on Pattern Analysis and Machine Intelligence, 2004, 26(2): 214-225