An introduction to nonlinear dimensionality reduction by maximum variance unfolding

Proceedings of the National Conference on Artificial Intelligence

Volumn 2, Issue , 2006, Pages 1683-1686

  Weinberger, Kilian Q ^a   Saul, Lawrence K ^a  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CONVEX OPTIMIZATION; HIGH DIMENSIONAL DATA; SENSORY INPUT; SYMBOLIC INPUT;

ALGORITHMS; DATA REDUCTION; LEARNING SYSTEMS; NONLINEAR SYSTEMS; OPTIMIZATION; PROBLEM SOLVING; STATISTICAL METHODS;

ARTIFICIAL INTELLIGENCE;

EID: 33750743381     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (12)

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2. Bowling, M.; Ghodsi, A.; and Wilkinson, D. 2005. Action respecting embedding. In Proceedings of the Twenty-second International Conference on Machine Learning (ICML 2005).
3. Burges, C. J. C. 2005. Geometric methods for feature extraction and dimensional reduction. In Rokach, L., and Maimon, O., eds., Data Mining and Knowledge Discovery Handbook: A Complete Guide for Practitioners and Researchers, Kluwer Academic Publishers.
4. Roweis, S. T., and Saul, L. K. 2000. Nonlinear dimensionality reduction by locally linear embedding. Science 290:2323-2326.
5. Saul, L. K.; Weinberger, K. Q.; Ham, J. H.; Sha, F.; and Lee, D. D. 2006. Spectral methods for dimensionality reduction. In B. Schoelkopf, O. C., and Zien, A., eds., Semisupervised Learning, MIT Press.
6. Sha, F., and Saul, L. K. 2005. Analysis and extension of spectral methods for nonlinear dimensionality reduction. In Proceedings of the Twenty-second International Conference on Machine Learning (ICML 2005).
7. Sun, J.; Boyd, S.; Xiao, L.; and Diaconis, P. 2006. The fastest mixing Markov process on a graph and a connection to a maximum variance unfolding problem. To appear in SIAM Review.
8. Tenenbaum, J. B.; de Silva, V.; and Langford, J. C. 2000. A global geometric framework for nonlinear dimensionality reduction. Science 290:2319-2323.
9. Vandenberghe, L., and Boyd, S. P. 1996. Semidefinite programming. SIAM Review 38(1):49-95.
10. Weinberger, K. Q., and Saul, L. K. 2004. Unsupervised learning of image manifolds by semidefinite programming. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR-04), volume 2, 988-995.
11. Weinberger, K. Q.; Packer, B. D.; and Saul, L. K. 2005. Nonlinear dimensionality reduction by semidefinite programming and kernel matrix factorization. In Ghahramani, Z., and Cowell, R., eds., Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics.
12. Weinberger, K. Q.; Sha, F.; and Saul, L. K. 2004. Learning a kernel matrix for nonlinear dimensionality reduction. In Proceedings of the Twenty First International Conference on Machine Learning (ICML-04), 839-846.