[1] YANG L P, GONG W G, GU X H, et al.. Complete discriminant locality preserving projections for face recognition[J]. Journal of Software, 2010,21(6):1277-1286. (in Chinese)
[2] YANG L P, GONG W G, LI W H, et al.. Random sampling subspace locality preserving projection for face recognition[J]. Opt. Precision Eng., 2008,16(8):129-124. (in Chinese)
[3] YANG L P, GONG W G, GU X H, et al.. Bagging null space locality preserving discriminant classifiers for face recognition[J]. Pattern Recognition, 2009, 42(9):1853-1858.
[4] DONG C, ZHAO H, WANG W, et al.. Hyperspectral image anomaly detection based on local orthogonal subspace projection[J]. Opt. Precision Eng., 2009,17(8):228-234. (in Chinese).
[6] TURK M, PENTLAND A. Eigenfaces for recognition [J]. Journal of Cognitive Neuroscience, 1991,3(1):71-86.
[7] BELHUMEUR P, HESPANFA J, KIREGEMAN D. Eigenfaces vs. fisherfaces: recognition using class specific linear projection [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997,19(7):711-720.
[8] TENENBAUM J B, SILVA V, LANGFORD J C. A global geometric framework for nonlinear dimensionality reduction [J]. Science, 2000, 290(5500):2319-2323.
[9] ROWEIS S T, SAUL L K. Nonlinear dimensionality reduction by locally linear embedding [J]. Science, 2000,290(5500):2323-2326.
[10] BELKIN M, NIYOGI P. Laplacian eigenmaps and spectral techniques for embedding and clustering [C]. Advances in Neural Information Processing Systems 14, Cambridge: MIT Press, 2002:585-591.
[11] HE X F, NIYOGI P. Locality preserving projections [J]. Advances in Neural Information Processing Systems 16, Cambridge: MIT Press, 2004:153-160.
[12] YAN S C, XU D, ZHANG B Y, et al.. Graph embedding and extensions: A general framework for dimensionality reduction[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007,29(1):40-51.
[13] LIU W, CHANG S F. Robust multi-class transductive learning with graphs [C]. IEEE Conference on Computer Vision and Pattern Recognition, Florida, 2009.
[14] BELKIN M, NIYOGI P. Laplacian eigenmaps for dimensionality reduction and data representation [J]. Neural Computation, 2003,15(6):1373-1396.
[15] QIAO L S, CHEN S C, TAN X Y. Sparsity preserving projections with applications to face recognition [J]. Pattern Recognition, 2010,43(1):331-341.
[16] ZHANG L M, QIAO L S, CHEN S C. Graph-optimized locality preserving projections [J]. Pattern Recognition, 2010,43(6): 1993-2002.
[17] CAI D, HE X F, HAN J W. Spectral regression for dimensionality reduction [R]. Illinois, 2007.
[18] BELHUMER P N, HESPANHA J, KREIGMAN D. Eigenfaces vs. fisherfaces: Recognition using class specific linear projection [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 17(7):711-720.
[19] ALLINSON N M. Face Recognition: From Theory to Applications [M]. Computer and Systems Sciences, 1998:446-456.
[20] 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.