Multi-task proximal support vector machine

Pattern Recognition

Volumn 48, Issue 10, 2015, Pages 3249-3257

  Li, Ya ^a   Tian, Xinmei ^a   Song, Mingli ^b   Tao, Dacheng ^c  

^a UNIVERSITY OF SCIENCE AND TECHNOLOGY OF CHINA   (China)

^b ZHEJIANG UNIVERSITY   (China)

^c UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

Author keywords

Multi task learning; Proximal classifiers; Support vector machines

Indexed keywords

CLASSIFICATION (OF INFORMATION); LEARNING SYSTEMS; SUPPORT VECTOR MACHINES; VECTORS;

CLASSIFICATION DATASETS; COMPUTATIONAL COSTS; EFFECTIVENESS AND EFFICIENCIES; EXPLOSIVE GROWTH; PROXIMAL CLASSIFIERS; PROXIMAL SUPPORT VECTOR MACHINES; SINGLE TASK LEARNING; VISUAL RECOGNITION;

MULTI-TASK LEARNING;

EID: 84931573460     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2015.01.014     Document Type: Article

References (44)

1. W. Hu, R. Hu, N. Xie, H. Ling, and S. Maybank Image classification using multiscale information fusion based on saliency driven nonlinear diffusion filtering IEEE Trans. Image Process.: Publ. IEEE Signal Process. Soc. 23 4 2014 1513 1526
2. L. Zhang, X. Zhen, and L. Shao Learning object-to-class kernels for scene classification IEEE Trans. Image Process. 23 8 2014 3241 3253
3. F. Zhu, Z. Jiang, L. Shao, Submodular object recognition, in: IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2014, pp. 2457-2464 doi:10.1109/CVPR.2014.315.
4. Q. Qiu, V.M. Patel, P. Turaga, R. Chellappa, Domain adaptive dictionary learning, in: Computer Vision - ECCV, 2012, pp. 631-645.
5. P. Turaga, A. Veeraraghavan, A. Srivastava, and R. Chellappa Statistical computations on Grassmann and Stiefel manifolds for image and video-based recognition IEEE Trans. Pattern Anal. Mach. Intell. 33 11 2011 2273 2286
6. X. Shen, Z. Lin, J. Brandt, and Y. Wu Spatially-constrained similarity measure for large-scale object retrieval IEEE Trans. Pattern Anal. Mach. Intell. 36 6 2014 1229 1241 10.1109/TPAMI.2013.237
7. A.J. Joshi, F. Porikli, N. Papanikolopoulos, Multi-class active learning for image classification, in: IEEE Conference on Computer Vision and Pattern Recognition, 2009, CVPR 2009, 2009, pp. 2372-2379.
8. Y.-H. Shao, W.-J. Chen, J.-J. Zhang, Z. Wang, and N.-Y. Deng An efficient weighted lagrangian twin support vector machine for imbalanced data classification Pattern Recognit. 47 9 2014 3158 3167
9. A. Tayal, T.F. Coleman, and Y. Li Primal explicit max margin feature selection for nonlinear support vector machines Pattern Recognit. 47 6 2014 2153 2164
10. Z. Jiang, G. Zhang, L.S. Davis, Submodular dictionary learning for sparse coding, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Washington, DC, USA, 2012, pp. 3418-3425.
11. F. Zhu, and L. Shao Weakly-supervised cross-domain dictionary learning for visual recognition Int. J. Comput. Vis. 109 1-2 2014 42 59
12. L. Shao, L. Liu, and X. Li Feature learning for image classification via multiobjective genetic programming IEEE Trans. Neural Netw. Learn. Syst. 25 7 2014 1359 1371
13. L. Shao, D. Wu, X. Li, Learning deep and wide: a spectral method for learning deep networks, IEEE Trans. Neural Netw. Learn. Syst. 25 (12) (2014) 2303-2308.
14. Y. Xue, X. Liao, L. Carlin, B. Krishnapuram, Multi-task learning for classification with Dirichlet process priors, J. Mach. Learn. Res. 8 (2007) 35-63.
15. R. Caruanal Multitask learning Mach. Learn. 28 1 1997 41 75 10.1023/A:1007379606734
16. S. Thrun, Learning to learn: introduction, in: Learning To Learn.
17. J. Baxter, A model of inductive bias learning, J. Artif. Intell. Res. 12(1-C12) (2000) 149-198.
18. P. Rai, H. Daume, Infinite predictor subspace models for multitask learning, in: International Conference on Artificial Intelligence and Statistics, 2010, pp. 613-620.
19. T. Evgeniou, M. Pontil, Regularized multi-task learning, in: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2004, pp. 109-117.
20. Y. Zhang, D.-Y. Yeung, A Convex Formulation for Learning Task Relationships in Multi-task Learning, arXiv:1203.3536.
21. S. Parameswaran, and K.Q. Weinberger Large margin multi-task metric learning Adv. Neural Inf. Process. Syst. 2010 1867 1875
22. K. Yu, V. Tresp, A. Schwaighofer, Learning gaussian processes from multiple tasks, in: Proceedings of the 22nd International Conference on Machine Learning, 2005, pp. 1012-1019.
23. S.-I. Lee, V. Chatalbashev, D. Vickrey, D. Koller, Learning a meta-level prior for feature relevance from multiple related tasks, in: Proceedings of the 24th International Conference on Machine Learning, 2007, pp. 489-496.
24. T. Jebara Multitask sparsity via maximum entropy discrimination J. Mach. Learn. Res. 12 2011 75 110
25. A. Argyriou, T. Evgeniou, and M. Pontil Convex multi-task feature learning Mach. Learn. 73 3 2008 243 272
26. Z. Kang, K. Grauman, F. Sha, Learning with whom to share in multi-task feature learning, in: Proceedings of the 28th International Conference on Machine Learning (ICML-11), 2011, pp. 521-528.
27. G. Fung, O. L. Mangasarian, Proximal support vector machine classifiers, in: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2001, pp. 77-86.
28. V. Vapnik The Nature of Statistical Learning Theory 2000 Springer
29. S. Ben-David, R. Schuller, Exploiting task relatedness for multiple task learning, in: Learning Theory and Kernel Machines, 2003, pp. 567-580.
30. T. Evgeniou, C.A. Micchelli, and M. Pontil Learning multiple tasks with kernel methods J. Mach. Learn. Res. 2005 615 637
31. B. Bakker, and T. Heskes Task clustering and gating for Bayesian multitask learning J. Mach. Learn. Res. 4 2003 83 99
32. J. Baxter, Learning internal representations, in: Proceedings of the Eighth Annual Conference on Computational Learning Theory, 1995, pp. 311-320.
33. T. Heskes Solving a huge number of similar tasks: a combination of multi-task learning and a hierarchical Bayesian approach ICML 1998 233 241
34. T. Heskes Empirical Bayes for learning to learn ICML 2000 367 374
35. J. Zhu, N. Chen, E. P. Xing, Infinite svm: a Dirichlet process mixture of large-margin kernel machines, in: Proceedings of the 28th International Conference on Machine Learning (ICML-11), 2011, pp. 617-624.
36. M. Lapin, B. Schiele, M. Hein, Scalable multitask representation learning for scene classification, in: 2014 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014, Columbus, OH, USA, June 23-28, 2014, pp. 1434-1441, doi:10.1109/CVPR.2014.186.
37. A. Jalali, P.D. Ravikumar, and S. Sanghavi A dirty model for multiple sparse regression IEEE Trans. Inf. Theory 59 12 2013 7947 7968 10.1109/TIT.2013.2280272
38. P. Gong, J. Ye, C. Zhang, Robust multi-task feature learning, Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (2012) 895-903.
39. J. Chen, J. Zhou, J. Ye, Integrating low-rank and group-sparse structures for robust multi-task learning, in: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2011, pp. 42-50.
40. G.M. Allenby, and P.E. Rossi Marketing models of consumer heterogeneity J. Economet. 89 1 1998 57 78
41. N. Arora, G.M. Allenby, and J.L. Ginter A hierarchical Bayes model of primary and secondary demand Market. Sci. 17 1 1998 29 44
42. G.M. Fung, and O.L. Mangasarian Multicategory proximal support vector machine classifiers Mach. Learn. 59 1-C2 2005 77 97
43. M.C. Ferris, and T.S. Munson Interior-point methods for massive support vector machines SIAM J. Optim. 13 3 2002 783 804
44. A. Vedaldi, and A. Zisserman Efficient additive kernels via explicit feature maps IEEE Trans. Pattern Anal. Mach. Intell. 34 3 2012 480 492