[1]TURK M, PENTLAND A.Eigenfaces for recognition [J]. Journal of Cognitive Neuroscience, 1991, 3(1):71-86.[2]BELHUMEUR P N, HESPANHA J P, KRIEGMAN D. Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7):711-720.[3]HE X F, NIYOGI P. Locality preserving projections [C]// 17th Annual Conference on Neural Information Processing System. Vancouver, Canada: MIT Press, 2003: 153-160.[4]YANG M H. Kernel eigenfaces vs. kernel fisherfaces: Face recognition using kernel methods[C]// IEEE International Conference on Automatic Face and Gesture Recognition. Washington, DC: IEEE Computer Society, 2002: 215-220.[5]BELKIN M, NIYOGI P. Laplacian eigenmaps for dimensionality reduction and data representation [J]. Neural Computation, 2003, 15(6): 1373-1396.[6]TENENBAUM J B, SILVA V D, LANGFORD J C. A global geometric framework for nonlinear dimensionality reduction [J]. Science, 2000, 290(5500): 2319-2323.[7]ROWELS S, SAUL L K. Nonlinear dimensionality reduction by locally linear embedding [J]. Science, 2000, 290(5500):2323-2326.[8]LEE D D, SEUNG H S. Learning the parts of objects by nonnegative matrix factorization [J]. Nature, 1999, 401: 788-791.[9]GUILLAMET D, VITRIA J. Non-negative matrix factorization for face recognition [C]// Proceedings of the 5th Catalonian Conference on AI: Topics in Artificial Intelligence. London:Springer-Verlag, 2002: 336-344.[10]LI S Z, HOU X W, ZHANG H J, et al. Learning spatially localized, part-based representation [C]// IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, DC: IEEE Computer Society, 2001: 207-212.[11]ZHANG T P, FANG B, TANG Y Y, et al.Topology preserving nonnegative matrix factorization for face recognition [J]. IEEE Transactions on Image Processing, 2008, 17(4):574-584.[12]WANG Y, JIA Y, HU C, et al. Fisher non-negative matrix factorization for learning local features [C]// Asian Conference on Computer Vision. Jeju: Society of Broadcast Engineers, 2004: 806-811.[13]LIANG ZHIZHENG, LI YOUFU, ZHAO TUO. Projected gradient method for kernel discriminant nonnegative matrix factorization and the applications [J]. Signal Processing, 2010, 90(7):2150-2163.[14]GUILLAMET D, VITRIA J, SCHIELE B. Introducing a weighted non-negative matrix factorization for image classification [J]. Pattern Recognition Letters, 2003, 24(14):2447-2454.[15]李乐, 章毓晋.基于线性投影结构的非负矩阵分解[J].自动化学报, 2010,36(1):23-39.[16]高涛,何明一. 改进投影梯度非负矩阵分解的单训练样本特征提取研究[J].电子与信息学报,2010,32(5):1121-1125.[17]LEE D D, SEUNG H S. Algorithms for non-negative matrix factorization [EB/OL].[2011-08-16].http://hebb.mit.edu/people/seung/papers/nmfconverge.pdf.[18]SIM T, BAKER S, BSAT M. The CMU pose, illumination, and expression database [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(12):1615-1618.[19]PHILLIPS P J, MOON H, RIZVI S A, et al. The FERET evaluation methodology for face-recognition algorithms [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(10):1090-1104.[20]YANG J, ZHANG D, FRANGI A F, et al. Two-dimensional PCA: A new approach to appearance based face representation and recognition [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(1): 131-137. |