[1] Jolliffe I T. Principal Component Analysis. New York:Springer Verlag, 1986. 1-2 http://www.scirp.org/reference/ReferencesPapers.aspx?ReferenceID=1356395
[2] Tao D P, Lin X, Jin L W, Li X L. Principal component 2-D long short-term memory for font recognition on single Chinese characters. IEEE Transactions on Cybernetics, 2015, 46(3):756-765 https://www.researchgate.net/publication/274394889_Principal_Component_2-D_Long_Short-Term_Memory_for_Font_Recognition_on_Single_Chinese_Characters
[3] Jolliffe I T, Cadima J. Principal component analysis:a review and recent developments. Philosophical Transactions of the Royal Society A:Mathematical, Physical and Engineering Sciences, 2016, 374(2065):20150202 doi: 10.1098/rsta.2015.0202
[4] 李伟, 焦松, 陆凌云, 杨明. 基于特征差异的仿真模型验证及选择方法. 自动化学报, 2014, 40(10):2134-2144 http://en.cnki.com.cn/Article_en/CJFDTOTAL-MOTO201410007.htm

Li Wei, Jiao Song, Lu Ling-Yun, Yang Ming. Validation and selection of simulation model based on the feature differences. Acta Automatica Sinica, 2014, 40(10):2134-2144 http://en.cnki.com.cn/Article_en/CJFDTOTAL-MOTO201410007.htm
[5] 韩敏, 许美玲, 任伟杰. 多元混沌时间序列的相关状态机预测模型研究. 自动化学报, 2014, 40(5):822-829 https://www.researchgate.net/publication/281891771_Research_on_multivariate_chaotic_time_series_prediction_using_mRSM_model

Han Min, Xu Mei-Ling, Ren Wei-Jie. Research on multivariate chaotic time series prediction using mRSM model. Acta Automatica Sinica, 2014, 40(5):822-829 https://www.researchgate.net/publication/281891771_Research_on_multivariate_chaotic_time_series_prediction_using_mRSM_model
[6] 樊继聪, 王友清, 秦泗钊. 联合指标独立成分分析在多变量过程故障诊断中的应用. 自动化学报, 2013, 39(5):494-501 https://www.researchgate.net/publication/271231082_Combined_Indices_for_ICA_and_Their_Applications_to_Multivariate_Process_Fault_Diagnosis

Fan Ji-Cong, Wang You-Qing, Qin S Joe. Combined indices for ICA and their applications to multivariate process fault diagnosis. Acta Automatica Sinica, 2013, 39(5):494-501 https://www.researchgate.net/publication/271231082_Combined_Indices_for_ICA_and_Their_Applications_to_Multivariate_Process_Fault_Diagnosis
[7] Zou H, Hastie T, Tibshirani R. Sparse principal component analysis. Journal of Computational and Graphical Statistics, 2006, 15(2):265-286 doi: 10.1198/106186006X113430
[8] Shen H P, Huang J Z. Sparse principal component analysis via regularized low rank matrix approximation. Journal of Multivariate Analysis, 2008, 99(6):1015-1034 doi: 10.1016/j.jmva.2007.06.007
[9] Johnstone I M, Lu A Y. On consistency and sparsity for principal components analysis in high dimensions. Journal of the American Statistical Association, 2009, 104(486):682-693 doi: 10.1198/jasa.2009.0121
[10] Journée M, Nesterov Y, Richtárik P, Sepulchre R. Generalized power method for sparse principal component analysis. The Journal of Machine Learning Research, 2010, 11:517-553 http://www.docin.com/p-721019144.html
[11] Croux C, Filzmoser P. Robust factorization of a data matrix. In:Proceedings of the 13th Computational Statistics Symposium. Bristol, Great Britain:Physica-Verlag HD, 1998. 245-250 http://cn.bing.com/academic/profile?id=aa85ede68a85c9d204516038d4913d33&encoded=0&v=paper_preview&mkt=zh-cn
[12] Brooks J P, Dulá J H, Boone E L. A pure L1-norm principal component analysis. Computational Statistics and Data Analysis, 2013, 61:83-98 doi: 10.1016/j.csda.2012.11.007
[13] Ke Q F, Kanade T. Robust L1 norm factorization in the presence of outliers and missing data by alternative convex programming. In:Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, CA, USA:IEEE, 2005, 1:739-746
[14] Ding C, Zhou D, He X F, Zha H Y. R1-PCA:rotational invariant L1-norm principal component analysis for robust subspace factorization. In:Proceedings of the 23rd International Conference on Machine Learning. Pittsburgh, USA:ACM, 2006. 281-288 http://cn.bing.com/academic/profile?id=aa5ba5364c85c7d92f7a58a86f9607bd&encoded=0&v=paper_preview&mkt=zh-cn
[15] Kwak N. Principal component analysis based on L1-norm maximization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008, 30(9):1672-1680 doi: 10.1109/TPAMI.2008.114
[16] Liang Z Z, Xia S X, Zhou Y, Zhang L, Li Y F. Feature extraction based on L_p-norm generalized principal component analysis. Pattern Recognition Letters, 2013, 34(9):1037-1045 doi: 10.1016/j.patrec.2013.01.030
[17] Kwak N. Principal component analysis by L_p-norm maximization. IEEE Transactions on Cybernetics, 2014, 44(5):594-609 doi: 10.1109/TCYB.2013.2262936
[18] Meng D Y, Zhao Q, Xu Z B. Improve robustness of sparse PCA by L1-norm maximization. Pattern Recognition, 2012, 45(1):487-497 doi: 10.1016/j.patcog.2011.07.009
[19] Wang R, Nie F P, Yang X J, Gao F F, Yao M L. Robust 2DPCA with non-greedy ι1-norm maximization for image analysis. IEEE Transactions on Cybernetics, 2015, 45(5):1108-1112
[20] Lu G F, Zou J, Wang Y. L1-norm and maximum margin criterion based discriminant locality preserving projections via trace Lasso. Pattern Recognition, 2016, 55:207-214 doi: 10.1016/j.patcog.2016.01.029
[21] Li C N, Shao Y H, Deng N Y. Robust L1-norm two-dimensional linear discriminant analysis. Neural Networks, 2015, 65:92-104 doi: 10.1016/j.neunet.2015.01.003
[22] Jolliffe I T, Trendafilov N T, Uddin M. A modified principal component technique based on the LASSO. Journal of Computational and Graphical Statistics, 2003, 12(3):531-547 doi: 10.1198/1061860032148
[23] Yu H, Yang J. A direct LDA algorithm for high-dimensional data——with application to face recognition. Pattern Recognition, 2001, 34(10):2067-2070 doi: 10.1016/S0031-3203(00)00162-X
[24] Yang J, Zhang D, Frangi A F, Yang J Y. Two-dimensional PCA:a new approach to appearance-based face representation and recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(1):131-137 doi: 10.1109/TPAMI.2004.1261097
[25] He X F, Yan S C, Hu Y X, Niyogi P, Zhang H J. Face recognition using Laplacianfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(3):328-340 doi: 10.1109/TPAMI.2005.55
[26] Cai D, He X, Han J, Zhang H J. Orthogonal Laplacianfaces for face recognition. IEEE Transactions on Image Processing, 2006, 15(11):3608-3614 doi: 10.1109/TIP.2006.881945