Top rank optimization in linear time

Advances in Neural Information Processing Systems

Volumn 2, Issue January, 2014, Pages 1502-1510

  Li, Nan ^a   Jin, Rong ^b   Zhou, Zhi Hua ^a  

^a NANJING UNIVERSITY   (China)

^b Michigan State University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA HANDLING;

COMPUTATIONAL COSTS; EMPIRICAL STUDIES; GENERALIZATION ERROR; LINEAR TIME; NEGATIVE INSTANCES; POSITIVE INSTANCES; RANKING FUNCTIONS; STATE-OF-THE-ART APPROACH;

INFORMATION SCIENCE;

EID: 84937934366     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (43)

1. S. Agarwal. The infinite push: A new support vector ranking algorithm that directly optimizes accuracy at the absolute top of the list. In SDM, pages 839-850, 2011.
2. S. Agarwal, T. Graepel, R. Herbrich, S. Har-Peled, and D. Roth. Generalization bounds for the area under the ROC curve. JMLR, 6: 393-425, 2005.
3. S. Boyd, C. Cortes, M. Mohri, and A. Radovanovic. Accuracy at the top. In NIPS, pages 962-970. 2012.
4. S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press, 2004.
5. C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, and G. Hullender. Learning to rank using gradient descent. In ICML, pages 89-96, 2005.
6. S. Clémençon, G. Lugosi, and N. Vayatis. Ranking and empirical minimization of U-statistics. Annals of Statistics, 36(2): 844-874, 2008.
7. S. Clémençon and N. Vayatis. Ranking the best instances. JMLR, 8: 2671-2699, 2007.
8. T. Cormen, C. Leiserson, R. Rivest, and C. Stein. Introduction to algorithms. MIT Press, 2001.
9. C. Cortes and M. Mohri. AUC optimization vs. error rate minimization. In NIPS, pages 313-320. 2004.
10. J. Duchi, S. Shalev-Shwartz, Y. Singer, and T. Chandra. Efficient projections onto the l1-ball for learning in high dimensions. In ICML, pages 272-279, 2008.
11. R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin. LIBLINEAR: A library for large linear classification. JMLR, 9: 1871-1874, 2008.
12. Y. Freund, R. Iyer, R. Schapire, and Y Singer. An efficient boosting algorithm for combining preferences. JMLR, 4: 933-969, 2003.
13. W. Gao, R. Jin, S. Zhu, and Z.-H. Zhou. One-pass AUC optimization. In ICML, pages 906-914, 2013.
14. M. Grant and S. Boyd. CVX: Matlab software for disciplined convex programming, version 2.1. http://cvxr.com/cvx, March 2014.
15. J. Hanley and B. McNeil. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143: 29-36, 1982.
16. R. Herbrich, T. Graepel, and K. Obermayer. Large Margin Rank Boundaries for Ordinal Regression, chapter Advances in Large Margin Classifiers, pages 115-132. MIT Press, Cambridge, MA, 2000.
17. T. Joachims. A support vector method for multivariate performance measures. In ICML, pages 377-384, Bonn, Germany, 2005.
18. T. Joachims. Training linear SVMs in linear time. In KDD, pages 217-226, 2006.
19. T. Kanamori, A. Takeda, and T. Suzuki. Conjugate relation between loss functions and uncertainty sets in classification problems. JMLR, 14: 1461-1504, 2013.
20. W. Kotlowski, K. Dembczynski, and E. Hüllermeier. Bipartite ranking through minimization of univariate loss. In ICML, pages 1113-1120, 2011.
21. Q.V. Le and A. Smola. Direct optimization of ranking measures. CoRR, abs/0704.3359, 2007.
22. N. Li, R. Jin, and Z.-H. Zhou. Top rank optimization in linear time. CoRR, abs/1410.1462, 2014.
23. N. Li, I. W. Tsang, and Z.-H. Zhou. Efficient optimization of performance measures by classifier adaptation. IEEE-PAMI, 35(6): 1370-1382, 2013.
24. J. Liu and J. Ye. Efficient Euclidean projections in linear time. In ICML, pages 657-664, 2009.
25. T.-Y. Liu. Learning to Rank for Information Retrieval. Springer, 2011.
26. H. Narasimhan and S. Agarwal. On the relationship between binary classification, bipartite ranking, and binary class probability estimation. In NIPS, pages 2913-2921. 2013.
27. H. Narasimhan and S. Agarwal. A structural SVM based approach for optimizing partial AUC. In ICML, pages 516-524, 2013.
28. H. Narasimhan and S. Agarwal. SVMpAUCtight: A new support vector method for optimizing partial AUC based on a tight convex upper bound. In KDD, pages 167-175, 2013.
29. A. Nemirovski. Efficient methods in convex programming. Lecture Notes, 1994.
30. Y. Nesterov. Introductory Lectures on Convex Optimization. Kluwer Academic Publishers, 2003.
31. A. Rakotomamonjy. Sparse support vector infinite push. In ICML, 2012.
32. S. Rendle, L. Balby Marinho, A. Nanopoulos, and L. Schmidt-Thieme. Learning optimal ranking with tensor factorization for tag recommendation. In KDD, pages 727-736, 2009.
33. C. Rudin and R. Schapire. Margin-based ranking and an equivalence between adaboost and rankboost. JMLR, 10: 2193-2232, 2009.
34. S. Shalev-Shwartz and Y. Singer. Efficient learning of label ranking by soft projections onto polyhedra. JMLR, 7: 1567-1599, 2006.
35. S. Sun and J. Shawe-Taylor. Sparse semi-supervised learning using conjugate functions. JMLR, 11: 2423-2455, 2010.
36. A. Tewari and P. Bartlett. On the consistency of multiclass classification methods. JMLR, 8: 1007-1025, 2007.
37. I. Tsochantaridis, T. Joachims, T. Hofmann, and Y. Altun. Large margin methods for structured and interdependent output variables. JMLR, 6: 1453-1484, 2005.
38. N. Usunier, D. Buffoni, and P. Gallinari. Ranking with ordered weighted pairwise classification. In ICML, pages 1057-1064, Montreal, Canada, 2009.
39. H. Valizadegan, R. Jin, R. Zhang, and J. Mao. Learning to rank by optimizing NDCG measure. In NIPS, pages 1883-1891. 2009.
40. M. Xu, Y.-F. Li, and Z.-H. Zhou. Multi-label learning with PRO loss. In AAAI, pages 998-1004, 2013.
41. T. Yang, Y.-F. Li, M. Mahdavi, R. Jin, and Z.-H. Zhou. Nyström method vs random Fourier features: A theoretical and empirical comparison. In NIPS, pages 485-493. MIT Press, 2012.
42. Y. Yue, T. Finley, F. Radlinski, and T. Joachims. A support vector method for optimizing average precision. In SIGIR, pages 271-278, 2007.
43. P. Zhao, S.C.H. Hoi, R. Jin, and T. Yang. Online AUC maximization. In ICML, pages 233-240, Bellevue, WA, 2011.