[1] Wise B M, Gallagher N B. The Process Chemometrics Approach to Process Monitoring and Fault Detection[J]. Journal of Process Control, 1996, 6(6): 329-348.
[2] 王志征, 余岳峰, 姚国平. 基于主成分分析法和自适应神经模糊推理系统的电力负荷预测[J]. 电力自动化设备, 2003, 23(9): 39-41.
[3] Kumar D, Kumar S, Rai C S. Feature Selection for Face Recognition: A Memetic Algorithmic Approach[J]. Journal of Zhejiang University: Science A, 2009, 10(8): 1140-1152.
[4] 李冬辉, 王乐英, 李 晟. 基于PCA的空调系统传感器故障诊断[J]. 电工技术学报, 2008, 23(6): 130-136.
[5] 汤红忠, 肖业伟, 黄辉先, 等. 基于PCA矢量形态学的彩色图像分割方法[J]. 计算机工程, 2009, 35(12): 201-203.
[6] Scholkopf B, Smola A J, MJuller K. Nonlinear Component Analysis As a Kernel Eigenvalue Problem[J]. Neural Computation, 1998, 10(5): 1299-1399.
[7] Choi S W, Lee C, Lee J M. Fault Detection and Identification of Nonlinear Processes Based on Kernel PCA[J]. Chemometrics and Intelligent Laboratory Systems, 2005, 75(1): 55-67.
[8] Kim K I, Jung K, Kim H J. Face Recognition Using Kernel Principal Component Analysis[J]. IEEE Signal Processing Letters, 2002, 9(2): 40-42.
[9] Santhanam A, Rahman M M. Kernel PCA in Detecting Moving Vehicle from Its Viewpoint[C]//Proc. of International Conference on Computing: Theory and Applications. Kolkata, India: [s. n.], 2007: 665-670.
[10] Teixeira A R, Tomé A M, Lang E W. Greedy KPCA in Biomedical Signal Processing[C]//Proc. of the 17th International Conference on Artificial Neural Networks. Porto, Portugal: [s. n.], 2007: 486-495.
[11] Cho H W. A Data Mining-based Subset Selection for Enhanced Discrimination Using Iterative Elimination of Redundancy[J]. Expert Systems with Applications, 2009, 36(2): 1355-1361.
[12] 付克昌, 吴铁军. 基于特征子空间的KPCA及其在故障检测与诊断中的应用[J]. 化工学报, 2006, 57(11): 2665-2669. |