Ordinal Regression via Manifold Learning

Proceedings of the 25th AAAI Conference on Artificial Intelligence, AAAI 2011

Volumn , Issue , 2011, Pages 398-403

  Liu, Yang ^a   Liu, Yan ^a   Chan, Keith C C ^a  

^a HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; LEARNING SYSTEMS;

DATA ITEMS; DATA SET; HIGH-DIMENSIONAL FEATURE SPACE; INTRINSIC GEOMETRY; MACHINE-LEARNING; MANIFOLD LEARNING; NONLINEAR STRUCTURE; ORDINAL REGRESSION; RATING SCALE; RESEARCH TOPICS;

REGRESSION ANALYSIS;

EID: 84865038689     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (27)

1. Belkin, M., and Niyogi, P. 2001. Laplacian eigenmaps and spectral techniques for embedding and clustering. In NIPS 14, 585-591.
2. Cai, D.; He, X.; and Han, J. 2007. Isometric projection. In AAAI.
3. Chu, W., and Keerthi, S. S. 2005. New approaches to support vector ordinal regression. In ICML, 145-152.
4. Crammer, K., and Singer, Y. 2002. Pranking with ranking. In NIPS 14, 641-647.
5. Dai, G., and Yeung, D.-Y. 2006. Tensor embedding methods. In AAAI, 330-335.
6. Fisher, R. A. 1936. The use of multiple measurements in taxonomic problems. Ann. Eugen. 7:179-188.
7. Frank, E., and Hall, M. 2001. A simple approach to ordinal classification. In ECML, 145-156.
8. Graham, D. B., and Allinson, N. M. 1998. Characterizing virtual eigensignatures for general purpose face recognition. In Face Recognition: From Theory to Applications, NATO ASI Series F, Computer and Systems Sciences Vol. 163, 446-456.
9. He, X., and Niyogi, P. 2004. Locality preserving projections. In NIPS 16.
10. He, X.; Cai, D.; Yan, S.; and Zhang, H.-J. 2005. Neighborhood preserving embedding. In ICCV, 1208-1213.
11. He, X.; Cai, D.; and Niyogi, P. 2006. Tensor subspace analysis. In NIPS 18. 499-506.
12. Hein, M., and Maier, M. 2007. Manifold denoising. In NIPS 19.
13. Herbrich, R.; Graepel, T.; and Obermayer, K. 2000. Large margin rank boundaries for ordinal regression. In Advances in Large Margin Classifiers, 115-132. MIT Press.
14. Hull, J. J. 1994. A database for handwritten text recognition research. IEEE Trans. Pattern Anal. Mach. Intell. 16(5):550-554.
15. Kramer, S.; Widmer, G.; Pfahringer, B.; and De Groeve, M. 2001. Prediction of ordinal classes using regression trees. Fundam. Inf. 47:1-13.
16. Li, L., and Lin, H.-T. 2007. Ordinal regression by extended binary classification. In NIPS 19, 865-872.
17. Li, X.; Lin, S.; Yan, S.; and Xu, D. 2008. Discriminant locally linear embedding with high-order tensor data. IEEE Trans. SMC-B 38(2):342-352.
18. Liu, W., and Chang, S.-F. 2009. Robust multi-class transductive learning with graphs. In CVPR, 381-388.
19. Liu, Y.; Liu, Y.; and Chan, K. C. C. 2009. Multilinear isometric embedding for visual pattern analysis. In ICCV Subspace Workshop, 212-218.
20. Roweis, S., and Saul, L. K. 2000. Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500):2323-2326.
21. Shashua, A., and Levin, A. 2003. Ranking with large margin principle: two approaches. In NIPS 15, 961-968.
22. Sun, B.-Y.; Li, J.; Wu, D. D.; Zhang, X.-M.; and Li, W.-B. 2010. Kernel discriminant learning for ordinal regression. IEEE Trans. Knowl. and Data Eng. 22:906-910.
23. Tenenbaum, J. B.; de Silva, V.; and Langford, J. C. 2000. A global geometric framework for nonlinear dimensionality reduction. Science 290(5500):2319-2323.
24. Wang, H.; Huang, H.; and Ding, C. H. Q. 2010. Discriminant laplacian embedding. In AAAI, 618-623.
25. Weinberger, K. Q., and Saul, L. K. 2006. An introduction to nonlinear dimensionality reduction by maximum variance unfolding. In Proc. 21st AAAI.
26. Yan, S.; Xu, D.; Yang, Q.; Zhang, L.; Tang, X.; and Zhang, H.-J. 2005. Discriminant analysis with tensor representation. In CVPR, volume 1, 526-532.
27. Zhou, D.; Weston, J.; Gretton, A.; Bousquet, O.; and Schölkopf, B. 2004. Ranking on data manifolds. In NIPS 16.