[1] [1] DU B, ZHANG L F, ZHANG L P, et al.. Discriminant manifold learning approach for hyperspectral image dimension reduction ［J］. ACTA PHOTONIC SINICA, 2013, 42(3): 320-325.(in Chinese)

[2] [2] CHEN H D, PU H H, WANG B, et al.. Image Euclidean distance-based manifold dimensionality reduction algorithm for hyperspectral imagery ［J］. Journal of Infrared and Millimeter Wave, 2013, 32(5): 450-455. (in Chinese)

[3] [3] SONG X F, LI J CH. Classification hyperspectral remote sensing image based on sparse representation and spectral information ［J］. Journal of Electronics & Information Technology, 2012, 34(2): 268-272. (in Chinese)

[4] [4] HUANG H, QU H P. Hyperspectral remote sensing image classification based on SSDE ［J］.Opt. Precision Eng., 2014, 22(2): 434-442.(in Chinese)

[5] [5] ABDI H, WILLIAMS L J. Principal component analysis ［J］.Wiley Interdisciplinary Reviews: Computational Statistics, 2010, 2(4): 433-459.

[6] [6] MOULIN C, LARGERON C, DUCOTTET C, et al.. Fisher linear discriminant analysis for text-image combination in multimedia information retrieval ［J］. Pattern Recognition, 2014, 47(1): 260-269.

[7] [7] WAN M, LAI Z, JIN Z. Feature extraction using two-dimensional local graph embedding based on maximum margin criterion ［J］. Applied Mathematics and Computation, 2011, 217(23): 9659-9668.

[8] [8] ROSMAN G, BRONSTEIN M M, BRONSTEIN A M, et al.. Nonlinear dimensionality reduction by topologically constrained isometric embedding［J］. International Journal of Computer Vision, 2010, 89(1): 56-68.

[9] [9] ZHAO X, ZHANG S. Facial expression recognition using local binary patterns and discriminant kernel locally linear embedding ［J］. EURASIP Journal on Advances in Signal Processing, 2012, 2012(1): 1-9.

[10] [10] LUO W. Face recognition based on Laplacian Eigenmaps ［C］.Proceedings of the 2011 IEEE International Conference on Computer Science and Service System (CSSS), Nanjing, China, 2011: 416-419.

[11] [11] HE X, CAI D, YAN S, et al.. Neighborhood preserving embedding ［C］.Proceedings of the 2005 IEEE International Conference on Computer Vision(ICCV), Beijing, china, 2005: 1208-1213.

[12] [12] HE X, NIYOGI P. Locality preserving projections ［C］.Proceedings of the Advances in Neural Information Processing Systems, Vancouver, BC, Canada, 2004: 234-241.

[13] [13] SONG Y Q, NIE F P, ZHANG C S, et al.. A unified framework for semi-supervised dimensionality reduction ［J］.Pattern Recognition, 2008, 41(9): 2789-2799.

[14] [14] SONG Y Q, NIE F P, ZHANG C S. Semi-supervised sub-manifold discriminant analysis ［J］.Pattern Recognition Letters, 2008, 29(13): 1806-1813.

[15] [15] BENGIO Y, MONPERRUS M. Non-local manifold tangent learning ［J］.Advances in Neural Information Processing Systems, 2005, 17: 129-136.

[16] [16] LI CH G, GUO J, ZHANG H G. Learning Bundle Manifold by Double Neighborhood Graphs ［M］.Computer Vision-ACCV 2009, Springer Berlin Heidelberg, 2010: 321-330.