Tenenbaum J B, de Silva V, Langford J C. A Global Geometric Framework for Nonlinear Dimensionality Reduction. Science, 2000, 290(5500): 2319-2323
[2]
Roweis S T, Saul L K. Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science, 2000, 290(5500): 2323-2326
[3]
Belkin M, Niyogi P. Laplacian Eigenmaps for Dimensionality Reduction and Data Representation. Neural Computation, 2003, 15(6): 1373-1396
[4]
Donoho D L, Grimes C. Hessian Eigenmaps: Locally Linear Embedding Techniques for High-Dimensional Data. Proc of the National Academy of Sciences of the United States of America, 2003, 100(10): 5591-5596
[5]
Zhang Z Y, Zha H Y. Principal Manifolds and Nonlinear Dimension Reduction via Local Tangent Space Alignment. SIAM Journal of Scientific Computing, 2004, 26(1): 313-338
[6]
Yang J, Li F X, Wang J. A Better Scaled Local Tangent Space Alignment Algorithm. Journal of Software, 2005, 16(9): 1584-1590 (in Chinese)(杨 剑,李伏欣,王 珏.一种改进的局部切空间排列算法.软件学报, 2005, 16(9): 1584-1590)
[7]
Bengio Y, Monperrus M. Non-Local Manifold Tangent Learning // Lawrence K S, Yair W, Léon B, eds. Advances in Neural Information Processing Systems. Cambridge, USA: MIT Press, 2005, 17: 129-136
[8]
de Ridder D, Kouropteva O, Okun O, et al. Supervised Locally Linear Embedding // Proc of the International Joint Conference on Artificial Neural Networks and Neural Information Processing. Istanbul, Turkey, 2003: 333-341
[9]
Hui K H, Wang C H. Clustering-Based Locally Linear Embedding // Proc of the 19th International Conference on Pattern Recognition. Tampa, USA, 2008: 1-4
[10]
Li H Y, Chen W B, Shen I F. Supervised Local Tangent Space Alignment for Classification // Proc of the 19th International Joint Conference on Artificial Intelligence. Edinburgh, UK, 2005: 1620-1621
[11]
Ma L, Crawford M M, Tian J W. Generalised Supervised Local Tangent Space Alignment for Hyperspectral Image Classification. Electronics Letters, 2010, 46(7): 497-498
[12]
Geng X, Zhan D C, Zhou Z H. Supervised Nonlinear Dimensionality Reduction for Visualization and Classification. IEEE Trans on Systems, Man and Cybernetics, 2005, 35(6): 1098-1107
[13]
Zhang S Q. Enhanced Supervised Locally Linear Embedding. Pattern Recognition Letters, 2009, 30(13): 1208-1218
[14]
Cheng Q C, Wang H Y, Liu A P, et al. A Multi-Manifold Learning Algorithm Based on ISOMAP. Microelectronics & Computer, 2009, 26(10): 115-121 (in Chinese)(程起才,王洪元,刘爱萍,等.基于ISOMAP的一种多流形学习算法.微电子学与计算机, 2009, 26(10): 115-121)
[15]
Ma R, Wang J X, Song Y X. Multi-Manifold Learning Using Locally Linear Embedding (LLE) Nonlinear Dimensionality Reduction. Journal of Tsinghua University: Science and Technology, 2008, 48(4): 583-586 (in Chinese) (马 瑞,王家廞,宋亦旭.基于局部线性嵌入(LLE)非线性降维的多流形学习.清华大学学报:自然科学版, 2008, 48(4): 583-586)
[16]
Wang Y, Jiang Y, Wu Y, et al. Multi-Manifold Clustering // Proc of the 11th Pacific Rim International Conference on Artificial Intelligence. Daequ, Republic of Korea, 2010: 280-291
[17]
Wang Y, Jiang Y, Wu Y, et al. Local and Structural Consistency for Multi-Manifold Clustering // Proc of the 22nd International Joint Conference on Artificial Intelligence. Barcelona, Spain, 2011, II: 1559-1564
[18]
Saxena A, Gupta A, Mukerjee A. Non-Linear Dimensionality Reduction by Locally Linear Isomaps // Proc of the 11th International Conference on Neural Information Processing. Calcutta, India, 2004: 1038-1043
[19]
Shao C, Wan C H, Chen G Y. ISOMAP Based on Minimal Connected Neighborhood Graph. Journal of Computer Applications, 2007, 27(10): 2570-2574 (in Chinese) (邵 超,万春红,陈广宇.基于最小连通邻域图的ISOMAP算法.计算机应用, 2007, 27(10): 2570-2574)
[20]
Shao C, Hu H T. Extension of ISOMAP for Imperfect Manifolds. Journal of Computers, 2012, 7(7): 1780-1785
[21]
Balasubramanian M, Shwartz E L. The ISOMAP Algorithm and Topological Stability. Science, 2002.