The optimization of the kind and parameters of kernel function in KPCA for process monitoring
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Ning Wang | Mingxing Jia | Xiaofei Liu | Hengyuan Xu | Mingxing Jia | Ning Wang | Hengyuan Xu | Xiaofei Liu
[1] Massimiliano Pontil,et al. Support Vector Machines for 3D Object Recognition , 1998, IEEE Trans. Pattern Anal. Mach. Intell..
[2] Julian Morris,et al. Nonlinear multiscale modelling for fault detection and identification , 2008 .
[3] C. I. Mosier. I. Problems and Designs of Cross-Validation 1 , 1951 .
[4] Yingwei Zhang,et al. Enhanced statistical analysis of nonlinear processes using KPCA, KICA and SVM , 2009 .
[5] Zhiqiang Ge,et al. Improved kernel PCA-based monitoring approach for nonlinear processes , 2009 .
[6] Steven M. LaValle,et al. On the Relationship between Classical Grid Search and Probabilistic Roadmaps , 2004, Int. J. Robotics Res..
[7] S. Wold. Cross-Validatory Estimation of the Number of Components in Factor and Principal Components Models , 1978 .
[8] Shufeng Tan,et al. Reducing data dimensionality through optimizing neural network inputs , 1995 .
[9] In-Beum Lee,et al. Fault identification for process monitoring using kernel principal component analysis , 2005 .
[10] Cheng-Lung Huang,et al. A GA-based feature selection and parameters optimizationfor support vector machines , 2006, Expert Syst. Appl..
[11] Chih-Jen Lin,et al. A Simple Decomposition Method for Support Vector Machines , 2002, Machine Learning.
[12] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[13] U. Kruger,et al. Moving window kernel PCA for adaptive monitoring of nonlinear processes , 2009 .
[14] Bernhard Schölkopf,et al. Nonlinear Component Analysis as a Kernel Eigenvalue Problem , 1998, Neural Computation.
[15] Gang Rong,et al. Nonlinear process monitoring based on maximum variance unfolding projections , 2009, Expert Syst. Appl..
[16] T. McAvoy,et al. Nonlinear principal component analysis—Based on principal curves and neural networks , 1996 .
[17] Ivan Dvořák,et al. Singular-value decomposition in attractor reconstruction: pitfalls and precautions , 1992 .
[18] Nello Cristianini,et al. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .
[19] Liangsheng Qu,et al. Evolving kernel principal component analysis for fault diagnosis , 2007, Comput. Ind. Eng..
[20] C. Yoo,et al. Nonlinear process monitoring using kernel principal component analysis , 2004 .
[21] Nagiza F. Samatova,et al. An SVM-based algorithm for identification of photosynthesis-specific genome features , 2003, Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003.
[22] S. Qin,et al. Selection of the Number of Principal Components: The Variance of the Reconstruction Error Criterion with a Comparison to Other Methods† , 1999 .
[23] M. Kramer. Nonlinear principal component analysis using autoassociative neural networks , 1991 .
[24] In-Beum Lee,et al. Nonlinear dynamic process monitoring based on dynamic kernel PCA , 2004 .
[25] Ricardo Massa Ferreira Lima,et al. GA-based method for feature selection and parameters optimization for machine learning regression applied to software effort estimation , 2010, Inf. Softw. Technol..
[26] Gunnar Rätsch,et al. Kernel PCA and De-Noising in Feature Spaces , 1998, NIPS.
[27] Engin Avci. Selecting of the optimal feature subset and kernel parameters in digital modulation classification by using hybrid genetic algorithm-support vector machines: HGASVM , 2009, Expert Syst. Appl..
[28] Thorsten Joachims,et al. Text categorization with support vector machines , 1999 .
[29] Mingtian Zhou,et al. Feature selection and parameter optimization for support vector machines: A new approach based on genetic algorithm with feature chromosomes , 2011, Expert Syst. Appl..
[30] Jin Hyun Park,et al. Fault detection and identification of nonlinear processes based on kernel PCA , 2005 .
[31] Ryo Saegusa,et al. Nonlinear principal component analysis to preserve the order of principal components , 2003, Neurocomputing.
[32] Wojtek J. Krzanowski,et al. Cross-Validation in Principal Component Analysis , 1987 .
[33] Gunnar Rätsch,et al. Input space versus feature space in kernel-based methods , 1999, IEEE Trans. Neural Networks.
[34] Zhao Chun-hui. New Nonlinear Principal Analysis Method Based on RBF Neural Network , 2007 .
[35] ChangKyoo Yoo,et al. Fault detection of batch processes using multiway kernel principal component analysis , 2004, Comput. Chem. Eng..
[36] Paul Geladi,et al. Principal Component Analysis , 1987, Comprehensive Chemometrics.
[37] Chih-Hung Wu,et al. A Novel hybrid genetic algorithm for kernel function and parameter optimization in support vector regression , 2009, Expert Syst. Appl..