Seung H S, Lee D D. The Manifold Ways of Perception. Science, 2000, 290(5500): 2268-2269
[2]
Roweis S T, Saul L K. Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science, 2000, 290(5500): 2323-2326
[3]
Tenenbaum J B, de Silva V, Langford J C. A Global Geometric Framework for Nonlinear Dimensionality Reduction. Science, 2000, 290(5500): 2319-2323
[4]
Pless R. Image Spaces and Video Trajectories: Using Isomap to Explore Video Sequences // Proc of the 9th IEEE International Conference on Computer Vision. Nice, France, 2003, Ⅱ: 1433-1440
[5]
Jenkins O C, Mataric M J. A Spatio-Temporal Extension to Isomap Nonlinear Dimension Reduction // Proc of the 21st International Conferrence on Machine Learing. Banff, Canada, 2004: 441-448
[6]
de Juan C, Bodenheimer B. Cartoon Textures // Proc of the Eurographics/ACM SIGGRAPH Symposium on Computer Animation. Grenoble, France, 2004: 267-276
[7]
Zhang J P, Li S Z, Wang J. Manifold Learning and Applications in Recognition // Tan Y P, Yap K H, Wang L, eds. Intelligent Multimedia Processing with Soft Computing. Heidelberg, Germany: Springer-Verlag, 2005: 281-300
[8]
Poggio T, Girosi F. Networks for Approximation and Learning. Proc of the IEEE, 1990, 78(9): 1481-1497
[9]
Elgammal A, Lee C S. Inferring 3D Body Pose from Silhouettes Using Activity Manifold Learning // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, USA, 2004, Ⅱ: 681-688
[10]
Kouropteva O, Okun O, Pietikainen M. Selection of the Optimal Parameter Value for the Locally Linear Embedding Algorithm // Proc of the 1st International Conference on Fuzzy Systems and Knowledge Discovery. Singapore, Singapore, 2002: 359-363
[11]
Bengio Y, Paiement J F, Vincent P. Out-of-Sample Extensions for LLE, Isomap, MDS, Eigenmaps and Spectral Clustering // Thrun S, Saul I K, Schlkopf B, eds. Advances in Neural Information Processing Systems 16. Cambridge, USA: MIT Press, 2004: 177-184
