Semi-Supervised Dimension Reduction for Multi-label Classification

Proceedings of the 24th AAAI Conference on Artificial Intelligence, AAAI 2010

Volumn , Issue , 2010, Pages 569-574

  Qian, Buyue ^a   Davidson, Ian ^a  

^a University of California Davis   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE;

DIMENSION REDUCTION; HIGH-DIMENSIONAL; HIGHER-DIMENSIONAL; LABELINGS; LARGE DATASETS; LEARNING TECHNIQUES; LOW DIMENSIONAL; MULTI-LABEL CLASSIFICATIONS; SEMI-SUPERVISED; SUPERVISED DIMENSION REDUCTIONS;

CLASSIFICATION (OF INFORMATION);

EID: 79960318399     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (14)

1. Belhumeur, P. N.; Hespanha, J. P.; Hespanha, P.; and Kriegman, D. J. 1997. Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 19:711-720.
2. Chang, C.-C., and Lin, C.-J. 2001. LIBSVM: a library for support vector machines.
3. Chen, G.; Song, Y.; Wang, F.; and Zhang, C. 2008. Semi-supervised multi-label learning by solving a sylvester equation. In Proceedings of the SIAM International Conference on Data Mining, 410-419.
4. Elisseeff, A. E., and Weston, J. 2001. A kernel method for multi-labelled classification. In In Advances in Neural Information Processing Systems 14, 681-687.
5. Finley, T., and Joachims, T. 2008. Training structural svms when exact inference is intractable. In ICML'08: Proceedings of the 25th international conference on Machine learning, 304-311.
6. Ji, S., and Ye, J. 2009. Linear dimensionality reduction for multi-label classification. In Proceedings of the 21st international jont conference on Artifical intelligence, 1077-1082.
7. Jolliffe, I. T. 2002. Principal Component Analysis. New York: Springer-Verlag, 2nd edition.
8. Rai, P., and Daume, H. 2009. Multi-label prediction via sparse infinite cca. In Advances in Neural Information Processing Systems 22. 1518-1526.
9. Roweis, S. T., and Saul, L. K. 2000. Nonlinear dimensionality reduction by locally linear embedding. Science 290:2323-2326.
10. Sun, L.; Ji, S.; and Ye, J. 2008. Hypergraph spectral learning for multi-label classification. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 668-676.
11. Yang, Y. 1999. An evaluation of statistical approaches to text categorization. Journal of Information Retrieval 1:67-88.
12. Yu, K.; Yu, S.; and Tresp, V. 2005. Multi-label informed latent semantic indexing. In Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, 258-265.
13. Zhang, Y., and Zhou, Z.-H. 2008. Multi-label dimensionality reduction via dependence maximization. In AAAI'08: Proceedings of the 23rd National Conference on Artificial Intelligence, 1503-1505.
14. Zhou, D.; Bousquet, O.; Lal, T. N.; Weston, J.; and Schlkopf, B. 2004. Learning with local and global consistency. In Advances in Neural Information Processing Systems, 321-328.