Efficient locally linear embeddings of imperfect manifolds

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 2734, Issue , 2003, Pages 188-201

  Hadid, Abdenour ^a   Pietikäinen, Matti ^a  

^a UNIVERSITY OF OULU   (Finland)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; DATA REDUCTION; ITERATIVE METHODS; NONLINEAR SYSTEMS; PRINCIPAL COMPONENT ANALYSIS; SPURIOUS SIGNAL NOISE; VISUALIZATION; ARTIFICIAL INTELLIGENCE; CLUSTERING ALGORITHMS; LEARNING SYSTEMS; PATTERN RECOGNITION; STATISTICS;

COORDINATE SYSTEMS; IMPERFECT MANIFOLDS; LOCALLY LINEAR EMBEDDINGS (LLE); EMBEDDINGS; GLOBAL COORDINATE SYSTEMS; HIGH DIMENSIONAL DATA; LLE ALGORITHMS; LOCALLY LINEAR EMBEDDING; NON-ITERATIVE; NONLINEAR DIMENSIONALITY REDUCTION; TRADITIONAL APPROACHES;

LINEAR SYSTEMS; DATA MINING;

EID: 8344231589     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/3-540-45065-3_17     Document Type: Conference Paper

References (21)

1. Roweis, S., Saul, L.: Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science v. (2000) 290, No. 5500, 2323-2326.
2. Saul, L., Roweis, S.: Think Globally, Fit Locally: Unsupervised Learning of Nonlinear Manifolds. Technical Report MS CIS-02-18, University of Pennsylvania. (2002).
3. Tenenbaum, J.B., De Silva, V., Langford, J.C.: A Global Geometric Framework for Nonlinear Dimensionality Reduction. Science v. (2000) 290, No. 5500, 2319-2323.
4. Jolliffe, I.T.: Principal Component Analysis. Springer-Verlag, New York (1986).
5. Cox, T., Cox, M. Multidimensional Scaling. Chapman & Hall, 2nd edition (2000).
6. Kohonen, T.: Self-Organizing Maps. Springer Series in Information Sciences, 3rd edition (2000).
7. Hastie, T.J., Stuetzle, W.: Principal Curves and Surfaces. Journal of the American Statistical Association (1689), 84(406), 502-516.
8. Verbeek, J.J., Vlassis, N., Kröse, B.: A K-segments Algorithm for Finding Principal Curves. Pattern Recognition Letters (2002b), 23(8), 1009-1017.
9. DeMers, D., Cottrell, G.W.: Nonlinear Dimensionality Reduction. Advances in Neural Information Processing Systems, San Mateo, CA (1993) volume 5, 580-587.
10. Bishop, C., Svensen, M., Williams, C.: GTM: The Generative Topographic Mapping. Neural Computation (1998), 10(1), 215-234.
11. De Backer, S., Naud, A., Scheunders, P.: Non-Linear Dimensionality Reduction Techniques for Unsupervised Feature Extraction. Pattern Recognition Letters (1998) 19, 711-720.
12. Li, S., De Vel, O., Coomans, D.: Comparative Performance Analysis of Non-Linear Dimensionality Reduction Methods. Technical Report, James Cook University (1995).
13. Hadid, A., Kouropteva, O., Pietikäinen, M.: Unsupervised Learning Using Locally Linear Embedding: Experiments in Face Pose Analysis. Proc. 16th International Conference on Pattern Recognition, Quebec, Canada (2002), 1, 111-114.
14. DeCoste. D.: Visualizing Mercel Kernel Feature Spaces via Kemelized Locally Linear Embedding. 8th International Conference on Neural Information Processing. (2001).
15. Vlachos, M., Domeniconi, C., Gunopulos, D., Kollios, G., Koudas, N.: Non-Linear Dimensionality Reduction Techniques for Classification and Visualization. Knowledge Discovery and Data Mining. Edmonton, Canada(2002).
16. Perona, P., Polito, M.: Grouping and Dimensionality Reduction by Locally Linear Embedding. Neural Information Processing Systems NIPS (2001).
17. Pless, R., Simon, I.: Embedding Images in Non-Flat Spaces. Technical Report WU-CS-01-43, Washington University, December (2001).
18. Ghahramani, Z., Hinton, G.E.: The EM Algorithm for Mixtures of Factor Analyzers. Technical Report CRG-TR-96-1, Department of Computer Science, University of Toronto (May 1996).
19. Kambhatla, N., Leen, T. K.: Dimension Reduction by Local Principal Component Analysis. Neural Computation (1997) 9, 1493-1516.
20. Ojala, T., Mäenpää, T., Pietikäinen, M., Viertola, J., Kyllönen, J., Huovinen, S.: Outex - New Framework for Empirical Evaluation of Texture Analysis Algorithms. Proc. 16th International Conference on Pattern Recognition, Quebec, Canada (2002), 1, 701-706. (http://www.outex. oulu.fi/outex.php).
21. Ojala, T., Pietikäinen, M., Harwood, D.: A Comparative Study of Texture Measures with Classification Based on Feature Distributions. Pattern Recognition (1996) 29, 51-59.