Automatically Outlier-Resisting Subspace Learning

[1] 刘青山, 卢汉清, 马颂德. 综述人脸识别中的子空间方法[J],自动化学报, 2003, 29(6): 900-911.Liu Qing-shan, Lu Han-qing, and Ma Sang-de. A Survey:subspace analysis for face recognition [J]. Acta AutomaticaSinica, 2003, 29(6): 900-911. [2] Turk M and Pentalnd A. Eigenfaces for recognition [J].Journal of Cognitive Neuroscience.1991, 3(1):71-86 [3] Belhumeur P, Hespanha J, and Kriengman D. Eigenfaces vs.fisherfaces: recognition using class specific linear projections[J]. IEEE Trans. on PAMI, 1997, 19(7): 771-720. [4] Bell A and Sejnowski T. An information-maximizationapproach to blind separation and blind deconvolution [J],Neural Computation, 1995, 7(6): 1129-1159. [5] 孙焕良, 鲍玉斌, 于戈, 等..一种基于划分的孤立点检测算法[J]. 软件学报, 2006, 17(5): 1009-1016.Jiang Hang-liang, Bao Yu-bin, and Yu Ge, et al.. Analgorithm based on partition for outlier detection [J]. Journalof Software, 2006, 17(5): 1009-1016. [6] Fernado T and Michael J. Robust principal componentanalysis for computer vision [C], Proc. IEEE ICCV,Vancouver, Canada, 2001: 1478-1485. [7] Xu L and Yuille A. Robust principal component analysis byself organizing rules based on statistical physics approach [J].IEEE Trans. on Neural Networks.1995, 6(1):131-143 [8] Li S, and Ju J. Face recognition using the nearest feature linemethod [J], IEEE Trans[J].on Neural Networks.1999, 10(2):439-443 [9] Phillips P and Moon H, et al.. The FERET evaluationmethodology for face recognition algorithms [J]. IEEE Trans.on PAMI, 2000, 22(10): 1090-1104