Probabilistic multi-label classification with sparse feature learning

IJCAI International Joint Conference on Artificial Intelligence

Volumn , Issue , 2013, Pages 1373-1379

  Guo, Yuhong ^a   Xue, Wei ^a  

^a Temple University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CRITICAL PROBLEMS; EMPIRICAL STUDIES; GRADIENT ALGORITHM; MULTI-LABEL CLASSIFICATIONS; NON-SMOOTH CONVEX OPTIMIZATIONS; OPTIMAL SOLUTIONS; PREDICTIVE MODELING; TEXT CATEGORIZATION;

CONVEX OPTIMIZATION; MODEL STRUCTURES; TEXT PROCESSING;

ARTIFICIAL INTELLIGENCE;

EID: 84896063365     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (33)

1. A. Argyriou, T. Evgeniou, and M. Pontil. Multi-task feature learning. In Proc. of NIPS, 2006.
2. F. Bach, R. Jenatton, J. Mairal, and G. Obozinski. Convex optimization with sparsity-inducing norms. In S. Sra, S. Nowozin, and S. Wright, editors, Optimization for Machine Learning. MIT Press, 2011.
3. A. Beck and M. Teboulle. A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J. on Imaging Sciences, 2(1):183-202, 2009.
4. C. Bielza, G. Li, and P. Larraòaga. Multi-dimensional classification with bayesian networks. Int. J. Approx. Reasoning, 52:705-727, 2011.
5. W. Chen, J. Yan, B. Zhang, Z. Chen, and Q. Yang. Document transformation for multi-label feature selection in text categorization. In Proc. of ICDM, 2007.
6. P. de Waal and L. van der Gaag. Inference and learning in multi-dimensional bayesian network classifiers. In Proc. of Euro. Conf. on Symb. and Quant. Appro. to Reasoning with Uncertainty, pages 501-511, 2007.
7. K. Dembczynski,W. Cheng, and E. Hüllermeier. Bayes optimal multilabel classification via probabilistic classifier chains. In Proc. of ICML, 2010.
8. A. Elisseeff and J. Weston. A kernel method for multi-labelled classification. In Proc. of NIPS, 2002.
9. J. Fuernkranz, E. Huellermeier, E. Mencia, and K. Brinker. Multilabel classification via calibrated label ranking. Machine Learning, 73(2), 2008.
10. N. Ghamrawi and A. Mc- Callum. Collective multi-label classification. In Proc. of CIKM, 2005.
11. S. Godbole and S. Sarawagi. Discriminative methods for multi-labeled classification. In Proc. of the Pacific-Asia Conference on KDDM, 2004.
12. Y. Guo and S. Gu. Multi-label classification using conditional dependency networks. In Proc. of IJCAI, 2011.
13. Y. Guo and D. Schuurmans. Adaptive large margin training for multilabel classification. In Proc. of AAAI, 2011.
14. B. Hariharan, L. Zelnik-Manor, SVN Vishwanathan, and M. Varma. Large scale maxmargin multi-label classification with priors. In Proc. of ICML, 2010.
15. S. Ji, L. Tang, S. Yu, and J. Ye. A sharedsubspace learning framework for multi-label classification. ACM Transactions on Knowledge Discovery from Data, 4, No. 2:1-29, 2010.
16. D. Lewis, Y. Yang, T. Rose, and F. Li. RCV1: A new benchmark collection for text categorization research. JMLR, 5:361-397, 2004.
17. J. Liu, S. Ji, and J. Ye. Multi-task feature learning via efficient L2,1-norm minimization. In Proc. of UAI, 2009.
18. A. Ng. Feature selection, L1 vs. L2 regularization, and rotational invariance. In Proc. of ICML, 2004.
19. F. Nie, H. Huang, X. Cai, and C. Ding. Efficient and robust feature selection via joint L2,1-norms minimization. In Proc. of NIPS, 2010.
20. G. Obozinski, B. Taskar, and M.I. Jordan. Multi-task feature selection. Technical report, Statistics Department, UC Berkeley, Tech. Rep, 2006.
21. J. Petterson and T. Caetano. Submodular multi-label learning. In Proc. of NIPS, 2011.
22. G. Qi, X. Hua, Y. Rui, J. Tang, T. Mei, and H. Zhang. Correlative multi-label video annotation. In Proc. of Multimedia, 2007.
23. R. Schapire and Y. Singer. Boostexter: A boosting-based system for text categorization. Machine Learning, 39(2):135-168, 2000.
24. S. Shalev-Shwartz and Y. Singer. Efficient learning of label ranking by soft projections onto polyhedra. JMLR, 7:1567-1599, 2006.
25. G. Tsoumakas and I. Katakis. Multi-label classification: an overview. Inter. J. of Data Warehous. and Mining, 3(3):1-13, 2007.
26. R. Yan, J. Tesic, and J. Smith. Modelshared subspace boosting for multi-label classification. In SIGKDD, 2007.
27. Y. Yang, H. Shen, Z. Ma, Z. Huang, and X. Zhou. L2,1-norm regularized discriminative feature selection for unsupervised learning. In Proc. of IJCAI, 2011.
28. K. Yu, S. Yu, and V. Tresp. Multi-label informed latent semantic indexing. In Proc. of the Annual ACM SIGIR Conference, 2005.
29. J. Zaragoza, L. Sucar, E. Morales, C. Bielza, and P. Larranaga. Bayesian chain classifiers for multidimensional classification. In Proc. of IJCAI, 2011.
30. M. Zhang and Z. Zhou. Multi-label dimensionality reduction via dependency maximization. In Proc. of AAAI, 2008.
31. Z. Zhou and M. Zhang. Multiinstance multi-label learning with application to scene classification. In Proc. of NIPS, 2006.
32. S. Zhu, X. Ji, W. Xu, and Y. Gong. Multilabelled classification using maximum entropy method. In Proc. of the Annual ACM SIGIR Conference, 2005.
33. H. Zou and T. Hastie. Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67(2):301-320, 2005.