Liu Q S, Lu H Q, Ma S D, A Survey: Subspace Analysis for Face Recognition. Acta Automatica Sinica, 2003, 29(6): 900-911 (in Chinese)(刘青山,卢汉清,马颂德.综述人脸识别中的子空间方法.自动化学报, 2003, 29(6): 900-911)
[2]
Turk M, Pentland A. Eigenfaces for Recognition. Journal of Cognitive Neuroscience, 1991, 3(1): 71-86
[3]
Belhumeur P N, Hespanha J P, Kriegman D J. Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE Trans on Pattern Analysis and Machine Intelligence, 1997, 19(7): 711-720
[4]
Shawe-Taylor J, Cristianini N. Kernel Methods for Pattern Analysis. 1st Edition. Cambridge, UK: Cambridge University Press, 2004
[5]
Schlkopf B, Smola A, Müller K R. Nonlinear Component Analysis as a Kernel Eigenvalue Problem. Neural Computation, 1998, 10(5): 1299-1319
[6]
Yang J, Gao X M, Zhang D, et al. Kernel ICA: An Alternative Formulation and Its Application to Face Recognition. Pattern Recognition, 2005, 38(10): 1784-1787
[7]
Yang M H. Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition Using Kernel Methods // Proc of the 5th IEEE International Conference on Automatic Face and Gesture Recognition. Washington, USA, 2002: 215-220
[8]
Pless R, Souvenir R. A Survey of Manifold Learning for Images. IPSJ Trans on Computer Vision and Applications, 2009, 1(1): 83-94
[9]
Roweis S T, Saul L K. Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science, 2000, 290(5500): 2323-2326
[10]
Tenenbaum J B, de Silva V, Langford J C. A Global Geometric Framework for Nonlinear Dimensionality Reduction. Science, 2000, 290(5500): 2319-2323
[11]
Belkin M, Niyogi P. Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering // Dietterich T G, Becker S, Ghah-ramani Z, eds. Advances in Neural Information Processing Systems. Cambridge, USA: MIT Press, 2001, 14: 585-591
[12]
He X F, Cai D, Yan S C, et al. Neighborhood Preserving Embedding // Proc of the 10th IEEE International Conference on Compu-ter Vision. Beijing, China, 2005, II: 1208-1213
[13]
Zhang D M, Fu M S, Luo B. Image Recognition with Two-Dimensional Neighbourhood Preserving Embedding. Pattern Recognition and Artificial Intelligence, 2011, 24(6): 810-815 (in Chinese)(张大明,符茂胜,罗 斌.基于二维近邻保持嵌入的图像识别.模式识别与人工智能, 2011, 24(6): 810-815)
[14]
Yang J, Zhang D, Frangi A F, et al. Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Re-cognition. IEEE Trans on Pattern Analysis and Machine Intelligence, 2004, 26(1): 131-137
[15]
Li M, Yuan B Z. 2D-LDA: A Statistical Linear Discriminant Analysis for Image Matrix. Pattern Recognition Letters, 2005, 26 (5): 527-532
[16]
Xiong H L, Swamy M N S, Ahmad M O. Two-Dimensional FLD for Face Recognition. Pattern Recognition, 2005, 38(7): 1121-1124
[17]
Hu D W, Feng G Y, Zhou Z T. Two-Dimensional Locality Preserving Projections (2DLPP) with Its Application to Palmprint Recognition. Pattern Recognition, 2007, 40(1): 339-342
[18]
Chen S B, Zhao H F, Kong M, et al. 2D-LPP: A Two Dimensional Extension of Locality Preserving Projections. Neurocomputing, 2007, 70(4/5/6): 912-921