Efficient max-margin multi-label classification with applications to zero-shot iearning

Machine Learning

Volumn 88, Issue 1-2, 2012, Pages 127-155

  Hariharan, Bharath ^a   Vishwanathan, S V N ^b   Varma, Manik ^c  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b PURDUE UNIVERSITY   (United States)

^c MICROSOFT RESEARCH   (United States)

Author keywords

1 vs all classification; Max margin methods; Multi label classification; SMO optimization; Zero shot learning

Indexed keywords

BOOK-KEEPING; DATA POINTS; INTERACTION TERM; LABEL SPACE; LEARNING SCENARIOS; LINEAR KERNEL; MAX-MARGIN METHODS; MULTI-LABEL; OPTIMISATIONS; ORDERS OF MAGNITUDE; PREDICTION FUNCTION; PROBLEM COMPLEXITY; STATE-OF-THE-ART ALGORITHMS; TEST SETS; TRAINING SETS; TRAINING TIME; ZERO-SHOT LEARNING;

OPTIMIZATION;

ALGORITHMS;

EID: 84865226076     PISSN: 08856125     EISSN: 15730565     Source Type: Journal    
DOI: 10.1007/s10994-012-5291-x     Document Type: Article

References (41)

1. Bernstein, D. S. (2005). Matrix mathematics. Princeton: Princeton University Press.
2. Boutell, M., Luo, J., Shen, X., & Brown, C. (2004). Learning multi-label scene classification. Pattern Recognition, 37(9), 1757-1771. (Pubitemid 38804465)
3. Cai, L., & Hofmann, T. (2007). Exploiting known taxonomies in learning overlapping concepts. In Proceedings of the international joint conference on artificial intelligence (pp. 714-719).
4. Cesa-Bianchi, N., Gentile, C., & Zaniboni, L. (2006). Incremental algorithms for hierarchical classification. Journal of Machine Learning Research, 7, 31-54. (Pubitemid 43022941)
5. Chang, C.-C., & Lin, C.-J. (2001). LIBSVM: A library for support vector machines. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.
6. Cheng, W., Dembczynski, K., & Huellermeier, E. (2010). Graded multilabel classification: The ordinal case. In Proceedings of the international conference on machine learning.
7. Crammer, K., & Singer, Y. (2003). A family of additive online algorithms for category ranking. Journal of Machine Learning Research, 3, 1025-1058.
8. Dekel, O., & Shamir, O. (2010). Multiclass-multilabel classification with more classes than examples. In Proceedings of the international conference on artificial intelligence and statistics.
9. Elisseeff, A., & Weston, J. (2001). A kernel method for multi-labelled classification. In Advances in neural information processing systems (pp. 681-687).
10. Evgeniou, T., Micchelli, C. A., & Pontil, M. (2005). Learning multiple tasks with kernel methods. Journal of Machine Learning Research.
11. Fan, R. E., Chen, P. H., & Lin, C. J. (2005).Working set selection using second order information for training SVM. Journal of Machine Learning Research, 6, 1889-1918.
12. Fan, R.-E., Chang, K.-W., Hsieh, C.-J., Wang, X.-R., & Lin, C.-J. (2008). Liblinear: A library for large linear classification. Journal of Machine Learning Research, 9, 1871-1874.
13. Farhadi, A., Endres, I., Hoeim, D., & Forsyth, D. A. (2009). Describing objects by their attributes. In Proceedings of the IEEE conference on computer vision and pattern recognition.
14. Farhadi, A., Endres, I., & Hoeim, D. (2010). Attribute-centric recognition for cross-category generalization. In Proceedings of the IEEE conference on computer vision and pattern recognition.
15. Hariharan, B., Vishwanathan, S. V. N., & Varma, M. (2010a). M3L code. http://research.microsoft.com/~manik/code/M3L/download.html.
16. Hariharan, B., Zelnik-Manor, L., Vishwanathan, S. V. N., & Varma, M. (2010b). Large scale max-margin multi-label classification with priors. In Proceedings of the international conference on machine learning.
17. Hsieh, C.-J., Chang, K.-W., Lin, C.-J., Keerthi, S. S., & Sundarajan, S. (2008). A dual coordinate descent method for large-scale linear SVM. In Proceedings of the international conference on machine learning.
18. Hsu, D., Kakade, S., Langford, J., & Zhang, T. (2009). Multi-label prediction via compressed sensing. In Advances in neural information processing systems.
19. Ji, S., Sun, L., Jin, R., & Ye, J. (2008).Multi-label multiple kernel learning. In Advances in neural information processing systems (pp. 777-784).
20. Keerthi, S. S., & Gilbert, E. G. (2002). Convergence of a generalized SMO algorithm for SVM classifier design. Machine Learning, 46(1-3), 351-360. (Pubitemid 34129975)
21. Lampert, C. H., Nickisch, H., & Harmeling, S. (2009). Learning to detect unseen object classes by betweenclass attribute transfer. In Proceedings of the IEEE conference on computer vision and pattern recognition.
22. Lewis, D., Yang, Y., Rose, T., & Li, F. (2004). RCV1: A new benchmark collection for text categorization research. Journal of Machine Learning Research, 5, 361-397.
23. Li, X.,Wang, L., & Sung, E. (2004).Multi-label SVM active learning for image classification. In Proceedings of the IEEE international conference on image processing (pp. 2207-2210). (Pubitemid 40820329)
24. Lin, C. J., Lucidi, S., Palagi, L., Risi, A., & Sciandrone,M. (2009). Decomposition algorithm model for singly linearly-constrained problems subject to lower and upper bounds. Journal of Optimization Theory and Applications, 141(1), 107-126.
25. McCallum, A. (1999). Multi-label text classification with a mixture model trained by EM. In AAAI 99 workshop on text learning.
26. Mitchell, T., Shinkareva, S., Carlson, A., Chang, K.-M., Malave, V., Mason, R., & Just, A. (2008). Predicting human brain activity associated with the meanings of nouns. Science, 320, 1191-1195. (Pubitemid 351929502)
27. Nocedal, J., & Wright, S. J. (2006). Numerical optimization (2nd ed.). Berlin: Springer.
28. Palatucci, M., Pomerleau, D., Hinton, G., & Mitchell, T. (2009). Zero-shot learning with semantic output codes. In Advances in neural information processing systems.
29. Platt, J. (1999). Fast training of support vector machines using sequential minimal optimization. In Advances in kernel methods -support vector learning (pp. 185-208).
30. Rifkin, R., & Khautau, A. (2004). In defense of one-vs-all classification. Journal of Machine Learning Research, 5, 101-141.
31. Rousu, J., Saunders, C., Szedmak, S., & Shawe-Taylor, J. (2006). Kernel-based learning of hierarchical multilabel classification models. Journal of Machine Learning Research, 7, 1601-1626. (Pubitemid 44024589)
32. Schapire, R. E., & Singer, Y. (2000). Boostexter: A boosting-based system for text categorization. Machine Learning, 39(2/3), 135-168. (Pubitemid 30594821)
33. Snoek, C., Worring, M., van Gemert, J., Geusebroek, J.-M., & Smeulders, A. (2006). The challenge problem for automated detection of 101 semantic concepts in multimedia. In Proceedings of ACM multimedia (pp. 421-430). (Pubitemid 47113558)
34. Taskar, B., Guestrin, C., & Koller, D. (2003). Max-margin Markov networks. In Advances in neural information processing systems. The SIAM Text Mining Competition (2007). http://www.cs.utk.edu/tmw07/.
35. Tsochantaridis, I., Joachims, T., Hofmann, T., & Altun, Y. (2005). Large margin methods for structured and interdependent output variables. Journal of Machine Learning Research, 6, 1453-1484.
36. Tsoumakas, G., & Katakis, I. (2007). Multi-label classification: An overview. International Journal of Data Warehousing and Mining, 3(3), 1-13.
37. Ueda, N., & Saito, K. (2003). Parametric mixture models for multi-labeled text. In Advances in neural information processing systems.
38. Vishwanathan, S. V. N., Sun, Z., Ampornpunt, N., & Varma, M. (2010). Multiple kernel learning and the SMO algorithm. In Advances in neural information processing systems 23.
39. Wang, G., Forsyth, D. A., & Hoeim, D. (2010). Comparative object similarity for improved recognition with few or no examples. In Proceedings of the IEEE conference on computer vision and pattern recognition.
40. Zhang, M.-L., & Wang, Z.-J. (2009a). Mimlrbf: Rbf neural networks for multi-instance multi-label learning. Neural Computing, 72(16-18), 3951-3956.
41. Zhang, M.-L. Wang, Z.-J. (2009b). Feature selection for multi-label naive Bayes classification. Information Sciences, 179(19), 3218-3229.