An efficient algorithm of learning the parametric map of locally linear embedding

Proceedings - 2008 2nd International Symposium on Intelligent Information Technology Application, IITA 2008

Volumn 3, Issue , 2008, Pages 52-56

  Zhang, Xu ^a   Liu, Yushu ^a   Gao, Chunxiao ^a   Liu, Jinghao ^b  

^a BEIJING INSTITUTE OF TECHNOLOGY   (China)

^b Qilusoft Park Development Center   (China)

Author keywords

[No Author keywords available]

Indexed keywords

INFORMATION TECHNOLOGY;

DATA POINTS; EFFICIENT ALGORITHMS; HIGH-DIMENSIONAL DATUM; IMAGE SPACES; LOCALLY LINEAR EMBEDDING; LOW-DIMENSIONAL MANIFOLDS; LOW-DIMENSIONAL SPACES; MULTIVARIATE LINEAR REGRESSIONS; PARAMETRIC FUNCTIONS; PERFORMANCE ANALYSIS;

LEARNING ALGORITHMS;

EID: 62949155116     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IITA.2008.331     Document Type: Conference Paper

References (12)

1. P.N. Belhumeur, J.P. Hespanha, D.J. Kriegman, Eigenfaces vs fishfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 19(7) (1997) 711-720
2. J.B. Tenenbaum, V. de Silva, and J.C. Langford, A Global Geometric Framework for Nonlinear Dimensionality Reduction, Science, vol. 290, 2000.
3. S.T. Roweis and L.K. Saul, Nonlinear Dimensionality Reduction by Locally Linear Embedding, Science, vol. 290, 2000.
4. L. Saul and S. Roweis. Think globally, fit locally: unsupervised learning of nonlinear manifolds. Journal of Machine Learning Research, 4:119-155, 2003. 15.
5. M. Belkin, P. Niyogi, Laplacian eigenmaps and spectral tecniques for embedding and clustering, Advances in neural information processing system 15, Vancouver, British Columbia, Canada, 2001
6. X.F. He, P. Niyogi, Locally preserving projection, in: Proceedings of the conference on Advances in Neural Information Processing System, 2003
7. D. de Ridder and R.P.W. Duin, Locally Linear Embedding for Classification, TR Series, No. PH-2002-01, PR Group, IS&T Dept., Delft Uni. of Tech., Netherlands, 2002.
8. th European Symp. on Artificial Neural Networks, Bruges, Belgium. (2003)
9. T. Sim, S. Baker, and M. Bsat, The CMU Pose, Illumination, and Expression (PIE) Database, Proc. IEEE Int'l Conf. Automatic Face and Gesture Recognition, May 2002.
10. Hastie, T., Tibshirani, R., & Friedman, J. The elements of statistical learning: Data mining, inference, and prediction. Springer, 2000.
11. Ye, J. Least Squares Linear Discriminant Analysis. Int. Conf. on Machine Learning Research, 7, 1087-1094, 2007.
12. F.C. Wu and Z.Y. Hu. The LLE and a linear mapping, Pattern Recognition 39 (2006), 1799-1804.