Correlated multi-label feature selection

International Conference on Information and Knowledge Management, Proceedings

Volumn , Issue , 2011, Pages 1087-1096

  Gu, Quanquan ^a   Li, Zhenhui ^a   Han, Jiawei ^a  

^a University of Illinois at Urbana Champaign   (United States)

Author keywords

cutting plane; dual coordinate descent; feature selection; label correlation; multi label learning

Indexed keywords

BENCHMARK DATA; COORDINATE DESCENT; CUTTING PLANE ALGORITHMS; CUTTING PLANES; FEATURE SELECTION METHODS; FEATURE SELECTION PROBLEM; HIGH DIMENSIONALITY; LEARNING METHODS; MINIMAX OPTIMIZATION; MIXED INTEGER PROGRAMMING; MULTI-LABEL; PRIOR DISTRIBUTION; WEIGHT VECTOR;

ECHO SUPPRESSION; INTEGER PROGRAMMING; KNOWLEDGE MANAGEMENT; NORMAL DISTRIBUTION;

FEATURE EXTRACTION;

EID: 83055191234     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2063576.2063734     Document Type: Conference Paper

References (39)

1. M. R. Boutell, J. Luo, X. Shen, and C. M. Brown. Learning multi-label scene classification. Pattern Recognition, 37(9):1757-1771, 2004. (Pubitemid 38804465)
2. S. Boyd and L. Vandenberghe. Convex optimization. Cambridge University Press, Cambridge, 2004.
3. G. Chen, Y. Song, F. Wang, and C. Zhang. Semi-supervised multi-label learning by solving a sylvester equation. In SDM, pages 410-419, 2008.
4. G. Chen, J. Zhang, F. Wang, C. Zhang, and Y. Gao. Efficient multi-label classification with hypergraph regularization. In CVPR, pages 1658-1665, 2009.
5. J. Chen and J. Ye. Training svm with indefinite kernels. In ICML, pages 136-143, 2008.
6. O. Dekel, C. D. Manning, and Y. Singer. Log-linear models for label ranking. In NIPS, 2003.
7. A. Elisseeff and J. Weston. A kernel method for multi-labelled classification. In NIPS, pages 681-687, 2001.
8. Q. Gu, Z. Li, and J. Han. Generalized fisher score for feature selection. In Proceedings of the International Conference on Uncertainty in Artificial Intelligence, 2011.
9. Q. Gu and J. Zhou. Subspace maximum margin clustering. In CIKM, pages 1337-1346, 2009.
10. A. K. Gupta and D. K. Nagar. Matrix Variate Distributions, volume 104 of Monographs and Surveys in Pure and Applied Mathematics. Chapman Hall/CRC, Florida, 2000.
11. B. Hariharan, L. Zelnik-Manor, S. V. N. Vishwanathan, and M. Varma. Large scale max-margin multi-label classification with priors. In ICML, pages 423-430, 2010.
12. C.-J. Hsieh, K.-W. Chang, C.-J. Lin, S. S. Keerthi, and S. Sundararajan. A dual coordinate descent method for large-scale linear svm. In ICML, pages 408-415, 2008.
13. S. Ji, L. Tang, S. Yu, and J. Ye. Extracting shared subspace for multi-label classification. In KDD, pages 381-389, 2008.
14. T. Joachims. Optimizing search engines using clickthrough data. In KDD, pages 133-142, 2002.
15. T. Joachims. Training linear svms in linear time. In KDD, pages 217-226, 2006.
16. T. Joachims, T. Finley, and C.-N. J. Yu. Cutting-plane training of structural svms. Machine Learning, 77(1):27-59, 2009.
17. J. E. Kelley. The cutting plane method for solving convex programs. Journal of the SIAM, 8:703-712, 1960.
18. S.-J. Kim and S. Boyd. A minimax theorem with applications to machine learning, signal processing, and finance. SIAM J. on Optimization, 19:1344-1367, November 2008.
19. D. D. Lewis, Y. Yang, T. G. Rose, and F. Li. Rcv1: A new benchmark collection for text categorization research. Journal of Machine Learning Research, 5:361-397, 2004.
20. Y. Li, I. Tsang, J. Kwok, and Z. Zhou. Tighter and convex maximum margin clusteringe. In AISTATS, 2009.
21. P. E. H. R. O. Duda and D. G. Stork. Pattern Classification. Wiley-Interscience Publication, 2001.
22. A. Rakotomamonjy, F. Bach, S. Canu, and Y. Grandvalet. More efficiency in multiple kernel learning. In ICML, pages 775-782, 2007.
23. R. M. Rifkin and A. Klautau. In defense of one-vs-all classification. Journal of Machine Learning Research, 5:101-141, 2004.
24. M. Rogati and Y. Yang. High-performing feature selection for text classification. In CIKM, pages 659-661, 2002.
25. S. Shalev-Shwartz, Y. Singer, and N. Srebro. Pegasos: Primal estimated sub-gradient solver for svm. In ICML, pages 807-814, 2007.
26. S. Sonnenburg, G. Rätsch, C. Schäfer, and B. Schölkopf. Large scale multiple kernel learning. Journal of Machine Learning Research, 7:1531-1565, 2006. (Pubitemid 44373694)
27. L. Sun, S. Ji, and J. Ye. Hypergraph spectral learning for multi-label classification. In KDD, pages 668-676, 2008.
28. M. Tan, L. Wang, and I. W. Tsang. Learning sparse svm for feature selection on very high dimensional datasets. In ICML, pages 1047-1054, 2010.
29. L. Tang, J. Chen, and J. Ye. On multiple kernel learning with multiple labels. In IJCAI, pages 1255-1260, 2009.
30. N. Ueda and K. Saito. Parametric mixture models for multi-labeled text. In NIPS, pages 721-728, 2002.
31. H. Wang, C. H. Q. Ding, and H. Huang. Multi-label linear discriminant analysis. In ECCV (6), pages 126-139, 2010.
32. Z. Xu, R. Jin, I. King, and M. R. Lyu. An extended level method for efficient multiple kernel learning. In NIPS, pages 1825-1832, 2008.
33. Y. Yang. An evaluation of statistical approaches to text categorization. Inf. Retr., 1(1-2):69-90, 1999.
34. Y. Yang and J. O. Pedersen. A comparative study on feature selection in text categorization. In ICML, pages 412-420, 1997.
35. K. Yu, S. Yu, and V. Tresp. Multi-label informed latent semantic indexing. In SIGIR, pages 258-265, 2005.
36. M.-L. Zhang and K. Zhang. Multi-label learning by exploiting label dependency. In KDD, pages 999-1008, 2010.
37. Y. Zhang and D. yan Yeung. A convex formulation for learning task relationships in multi-task learning. In Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence (UAI), 2010.
38. Y. Zhang and D.-Y. Yeung. Transfer metric learning by learning task relationships. In KDD, pages 1199-1208, 2010.
39. Y. Zhang and Z.-H. Zhou. Multi-label dimensionality reduction via dependence maximization. In AAAI, pages 1503-1505, 2008.