[1] 李春娜, 陈伟杰, 邵元海.鲁棒的稀疏Lp-模主成分分析.自动化学报, 2017, 43(1): 142-151 doi: 10.16383/j.aas.2017.c150512

Li Chun-Na, Chen Wei-Jie, Shao Yuan-Hai. Robust sparse Lp-norm principal component analysis. Acta Automatica Sinica, 2017, 43(1): 142-151 doi: 10.16383/j.aas.2017.c150512
[2] 张先鹏, 陈帆, 和红杰.结合多种特征的高分辨率遥感影像阴影检测.自动化学报, 2016, 42(2): 290-298 doi: 10.16383/j.aas.2016.c150196

Zhang Xian-Peng, Chen Fan, He Hong-Jie. Shadow detection in high resolution remote sensing images using multiple features. Acta Automatica Sinica, 2016, 42(2): 290-298 doi: 10.16383/j.aas.2016.c150196
[3] 董恩增, 魏魁祥, 于晓, 冯倩.一种融入PCA的LBP特征降维车型识别算法.计算机工程与科学, 2017, 39(2): 359-363 doi: 10.3969/j.issn.1007-130X.2017.02.021

Dong En-Zeng, Wei Kui-Xiang, Yu Xiao, Feng Qian. A model recognition algorithm integrating PCA into LBP feature dimension reduction. Computer Engineering and Science, 2017, 39(2): 359-363 doi: 10.3969/j.issn.1007-130X.2017.02.021
[4] Wan M, Shang W L, Zeng P. Double behavior characteristics for one-class classification anomaly detection in networked control systems. IEEE Transactions on Information Forensics and Security, 2017, 12(12): 3011-3023 doi: 10.1109/TIFS.2017.2730581
[5] Chen B J, Yang J H, Jeon B, Zhang X P. Kernel quaternion principal component analysis and its application in RGB-D object recognition. Neurocomputing, 2017, 266: 293-303 doi: 10.1016/j.neucom.2017.05.047
[6] 赵孝礼, 赵荣珍.全局与局部判别信息融合的转子故障数据集降维方法研究.自动化学报, 2017, 43(4): 560-567 doi: 10.16383/j.aas.2017.c160317

Zhao Xiao-Li, Zhao Rong-Zhen. A method of dimension reduction of rotor faults data set based on fusion of global and local discriminant information. Acta Automatica Sinica, 2017, 43(4): 560-567 doi: 10.16383/j.aas.2017.c160317
[7] 吴枫, 仲妍, 吴泉源.基于增量核主成分分析的数据流在线分类框架.自动化学报, 2010, 36(4): 534-542 doi: 10.3724/SP.J.1004.2010.00534

Wu Feng, Zhong Yan, Wu Quan-Yuan. Online classification framework for data stream based on incremental kernel principal component analysis. Acta Automatica Sinica, 2010, 36(4): 534-542 doi: 10.3724/SP.J.1004.2010.00534
[8] 吴广宁, 袁海满, 高波, 李帅兵.基于特征评估与核主元分析的电力变压器故障诊断.高电压技术, 2017, 43(8): 2533-2540 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=gdyjs201708013

Wu Guang-Ning, Yuan Hai-Man, Gao Bo, Li Shuai-Bing. Fault diagnosis of power transformer based on feature evaluation and kernel principal component analysis. High Voltage Engineering, 2017, 43(8): 2533-2540 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=gdyjs201708013
[9] Huang J P, Yan X F. Quality relevant and independent two block monitoring based on mutual information and KPCA. IEEE Transactions on Industrial Electronics, 2017, 64(8): 6518-6527 doi: 10.1109/TIE.2017.2682012
[10] Xie H B, Zhou P, Guo T R, Sivakumar B, Zhang X, Dokos S. Multiscale two-directional two-dimensional principal component analysis and its application to high-dimensional biomedical signal classification. IEEE Transactions on Biomedical Engineering, 2016, 63(7): 1416-1425 doi: 10.1109/TBME.2015.2436375
[11] Xia J S, Falco N, Benediktsson J A, Du P J, Chanussot J. Hyperspectral image classification with rotation random forest via KPCA. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(4): 1601-1609 doi: 10.1109/JSTARS.2016.2636877
[12] 阳同光, 桂卫华.基于KPCA与RVM感应电机故障诊断研究.电机与控制学报, 2016, 20(9): 89-95 http://d.old.wanfangdata.com.cn/Periodical/djykzxb201609013

Yang Tong-Guang, Gui Wei-Hua. Research on fault diagnosis of induction motor based KPCA and RVM. Electric Machines and Control, 2016, 20(9): 89-95 http://d.old.wanfangdata.com.cn/Periodical/djykzxb201609013
[13] Wu X, Nie L, Xu M. Robust fuzzy quality function deployment based on the mean-end-chain concept: service station evaluation problem for rail catering services. European Journal of Operational Research, 2017, 263(3): 974-995 doi: 10.1016/j.ejor.2017.05.036
[14] Gao X K, Lee H M, Gao S P. A robust parameter design of wide band DGS filter for common-mode noise mitigation in high-speed electronics. IEEE Transactions on Electromagnetic Compatibility, 2017, 59(6): 1735-1740 doi: 10.1109/TEMC.2017.2710202
[15] Choi S W, Park J H, Lee I B. Process monitoring using a Gaussian mixture model via principal component analysis and discriminant analysis. Computers & Chemical Engineering, 2004, 28(8): 1377-1387 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=65dba41b16cab1ad18e6181b8673da19
[16] Raveendran R, Huang B. Two layered mixture Bayesian probabilistic PCA for dynamic process monitoring. Journal of Process Control, 2017, 57: 148-163 doi: 10.1016/j.jprocont.2017.06.009
[17] Huang S Y, Yen Y R, Eguchi S. Robust kernel principal component analysis. Neural Computation, 2009, 21(11): 3179-3213 doi: 10.1162/neco.2009.02-08-706
[18] Huang H H, Yen Y R. An iterative algorithm for robust kernel principal component analysis. Neurocomputing, 2011, 74(18): 3921-3930 doi: 10.1016/j.neucom.2011.08.008
[19] Heo G, Gader P, Frigui H. RKF-PCA: robust kernel fuzzy PCA. Neural Networks, 2009, 22(5-6): 642-650 doi: 10.1016/j.neunet.2009.06.013
[20] 陶新民, 刘福荣, 刘玉, 童智靖.一种多尺度协同变异的粒子群优化算法.软件学报, 2012, 23(7): 1805-1815 http://d.old.wanfangdata.com.cn/Periodical/rjxb201207013

Tao Xin-Min, Liu Fu-Rong, Liu Yu, Tong Zhi-Jing. Multi-scale cooperative mutation particle swarm optimization algorithm. Journal of Software, 2012, 23(7): 1805-1815 http://d.old.wanfangdata.com.cn/Periodical/rjxb201207013
[21] 张航, 叶东毅.一种基于多正则化参数的矩阵分解推荐算法.计算机工程与应用, 2017, 53(3): 74-79 http://d.old.wanfangdata.com.cn/Periodical/jsjgcyyy201703014

Zhang Hang, Ye Dong-Yi. Recommender algorithm based on matrix factorization with multiple regularization parameters. Computer Engineering and Application, 2017, 53(3): 74-79 http://d.old.wanfangdata.com.cn/Periodical/jsjgcyyy201703014
[22] 陶新民, 徐晶, 杨立标, 刘玉.一种改进的粒子群和K均值混合聚类算法.电子与信息学报, 2010, 32(1): 92-97 http://d.old.wanfangdata.com.cn/Periodical/dzkxxk201001017

Tao Xin-Min, Xu Jing, Yang Li-Biao, Liu Yu. Improved cluster algorithm based on K-means and particle swarm optimization. Journal of Electronics & Information Technology, 2010, 32(1): 92-97 http://d.old.wanfangdata.com.cn/Periodical/dzkxxk201001017
[23] 程昊翔, 王坚.基于快速聚类分析的支持向量数据描述算法.控制与决策, 2016, 31(3): 551-554 http://d.old.wanfangdata.com.cn/Periodical/kzyjc201603025

Cheng Hao-Xiang, Wang Jian. Support vector data description based on fast clustering analysis. Control and Decision, 2016, 31(3): 551-554 http://d.old.wanfangdata.com.cn/Periodical/kzyjc201603025
[24] 郑祺, 黄德才.基于引力相似度和相对密度的不确定数据流聚类.上海交通大学学报, 2016, 50(6): 873-878 http://d.old.wanfangdata.com.cn/Periodical/shjtdxxb201606010

Zheng Qi, Huang De-Cai. Uncertain data stream clustering algorithm based on gravity similarity and relative density techniques. Journal of Shanghai Jiaotong University, 2016, 50(6): 873-878 http://d.old.wanfangdata.com.cn/Periodical/shjtdxxb201606010
[25] Feature selection datasets[Online], availalde: http://featureselection.asu.edu/datasets.php, December 1, 2019