MI2LS: Multi-instance learning from multiple information sources

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn Part F128815, Issue , 2013, Pages 149-157

  Zhang, Dan ^a   He, Jingrui ^b   Lawrence, Richard D ^c  

^a FACEBOOK   (United States)

^b Stevens Institute of Technology   (United States)

^c IBM T J WATSON RESEARCH CENTER   (United States)

Author keywords

Multi instance learning; Multi view learning; Stoachastic gradient descent

Indexed keywords

CLASSIFICATION (OF INFORMATION); CONVEX OPTIMIZATION; DATA MINING; INFORMATION RETRIEVAL SYSTEMS; LEARNING SYSTEMS;

CLASSIFICATION PERFORMANCE; DOCUMENT CLASSIFICATION; GRADIENT DESCENT; INSIDER THREAT DETECTIONS; MULTI-INSTANCE LEARNING; MULTI-VIEW LEARNING; MULTIPLE-INSTANCE LEARNING; TRADITIONAL LEARNING;

EDUCATION;

EID: 85014560717     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2487575.2487651     Document Type: Conference Paper

References (51)

1. S. Ali and M. Shah. Human action recognition in videos using kinematic features and multiple instance learning. IEEE Trans. Pattern Anal. Mach. Intell., 32(2):288-303, 2010.
2. S. Andrews, I. Tsochantaridis, and T. Hofmann. Support vector machines for multiple-instance learning. In NIPS, 2003.
3. B. Babenko, M.-H. Yang, and S. Belongie. Robust object tracking with online multiple instance learning. IEEE Trans. Pattern Anal. Mach. Intell., 33(8):1619-1632, 2011.
4. C. Bergeron, G. M. Moore, J. Zaretzki, C. M. Breneman, and K. P. Bennett. Fast bundle algorithm for multipleinstance learning. IEEE Trans. Pattern Anal. Mach. Intell., 34(6):1068-1079, 2012.
5. A. Blum and T. M. Mitchell. Combining labeled and unlabeled sata with co-Training. In COLT, pages 92-100, 1998.
6. B.Scholkopf and A.Smola. Learning with Kernels. MITPress, Cambridge, MA, 2002.
7. R. C. Bunescu and R. J. Mooney. Multiple instance learning for sparse positive bags. In ICML, pages 105-112, 2007.
8. Y. Chen, J. Bi, and J. Z. Wang. Miles: Multiple-instance learning via embedded instance selection. IEEE Trans. Pattern Anal. Mach. Intell., 28(12):1931-1947, 2006.
9. Y. Chen and J. Z. Wang. Image categorization by learning and reasoning with regions. Journal of Machine Learning Research, 5:913-939, 2004.
10. T. Deselaers and V. Ferrari. A conditional random field for multiple-instance learning. In ICML, pages 287-294, 2010.
11. T. G. Dietterich, R. H. Lathrop, and T. Lozano-Perez. Solving the multiple instance problem with axis-parallel rectangles. In Artificial Intelligence, 1998.
12. J. Farquhar, D. Hardoon, H. Meng, J. Shawe-Taylor, and S. Szedmak. Two view learning: SVM-2K, theory and practice. NIPS, 18:355, 2006.
13. A. Fuduli, M. Gaudioso, and G. Giallombardo. Minimizing non convex non smooth functions via cutting planes and proximity control. SIAM Journal on Optimization, 14(3):743-756, 2004.
14. T. Gärtner, P. Flach, A. Kowalczyk, and A. Smola. Multi- instance kernels. In ICML, 2002.
15. T. Gärtner, P. A. Flach, A. Kowalczyk, and A. J. Smola. Multiinstance kernels. In ICML, pages 179-186, 2002.
16. P. V. Gehler and O. Chapelle. Deterministic annealing for multiple-instance learning. Journal of Machine Learning Research - Proceedings Track, 2:123-130, 2007.
17. J. He and R. Lawrence. A graphbased framework for multitask multi-view learning. In ICML, pages 25-32, 2011.
18. T. Hofmann. Probabilistic latent semantic indexing. In SIGIR, pages 50-57, 1999.
19. Y. Hu, M. Li, and N. Yu. Multiple-instance ranking: Learning to rank images for image retrieval. In CVPR, 2008.
20. R. Jin, S. Wang, and Z.-H. Zhou. Learning a distance metric from multi-instance multi-label data. In CVPR, pages 896- 902, 2009.
21. T. Joachims. Training linear svms in linear time. In KDD, pages 217-226, 2006.
22. T. Joachims and N. Cristianini. Composite kernels for hypertext categorisation. In ICML, pages 250-257, 2001.
23. M. Kim and F. D. la Torre. Gaussian processes multiple instance learning. In ICML, pages 535-542, 2010.
24. J. T. Kwok and P.-M. Cheung. Marginalized multi-instance kernels. In IJCAI, pages 901-906, 2007.
25. G. Li, S. C. H. Hoi, and K. Chang. Two-view transductive support vector machines. In SDM, pages 235-244, 2010.
26. Y.-X. Li, S. Ji, S. Kumar, J. Ye, and Z.-H. Zhou. Drosophila gene expression pattern annotation through multi-instance multi-label learning. IEEE/ACM Trans. Comput. Biology Bioinform., 9(1):98-112, 2012.
27. D. G. Lowe. Object recognition from local scale-invariant features. In ICCV, pages 1150-1157, 1999.
28. C. D. Manning, P. Raghavan, and H. Schtze. Introduction to Information Retrieval. Cambridge University Press, 2008.
29. O. Maron and T. Lozano-Pérez. A framework for multipleinstance learning. In NIPS, 1997.
30. O. Maron and A. L. Ratan. Multiple-instance learning for natural scene classification. In ICML, pages 341-349, 1998.
31. M. Mayo and E. Frank. Experiments with multi-view multi instance learning for supervised image classification. In Proc 26th International Conference Image and Vision Computing New Zealand, Auckland, New Zealand, pages 363-369, 2011.
32. R. Rahmani and S. Goldman. MISSL: Multiple-instance semi-supervised learning. In ICML, 2006.
33. R. Rahmani, S. A. Goldman, H. Zhang, S. R. Cholleti, and J. E. Fritts. Localized content-based image retrieval. IEEE Trans. Pattern Anal. Mach. Intell., 30(11):1902-1912, 2008.
34. A. Rakhlin, O. Shamir, and K. Sridharan. Making gradient descent optimal for strongly convex stoachastic optimization. In ICML, 2012.
35. D. Rosenberg, V. Sindhwani, P. Bartlett, and P. Niyogi. A Kernel for Semi-Supervised Learning With Multi-View Point Cloud Regularization. IEEE Signal Processing Magazine, 2009.
36. S. Shalev-shwartz and Y. Singer. Logarithmic regret algorithms for strongly convex repeated games. In The Hebrew University, 2007.
37. S. Shalev-Shwartz, Y. Singer, N. Srebro, and A. Cotter. Pegasos: primal estimated sub-gradient solver for svm. Math. Program., 127(1):3-30, 2011.
38. J. Shawe-Taylor and N. Cristianini. Kernel Methods for Pattern Analysis. MCambridge University Press, 2004.
39. V. Sindhwani and P. Niyogi. A co-regularized approach to semi-supervised learning with multiple views. In ICMLWorkshop on Learning with Multiple Views, 2005.
40. C. H. Teo, S. V. N. Vishwanathan, A. J. Smola, and Q. V. Le. Bundle methods for regularized risk minimization. Journal of Machine Learning Research, 11:311-365, 2010.
41. A. S. Vishwanathan, A. J. Smola, and S. V. N. Vishwanathan. Kernel methods for missing variables. In AISTATS, pages 325-332, 2005.
42. J.Wang, et Jean-Daniel Zucker, and J. daniel Zucker. Solving the multiple-instance problem: A lazy learning approach. In ICML, pages 1119-1125, 2000.
43. M. Wu and B. Schölkopf. A local learning approach for clustering. In NIPS, pages 1529-1536, 2006.
44. O.Wu, J. Gao, W. Hu, B. Li, and M. Zhu. Indentifying multi instance outliers. In SDM, pages 430-441, 2010.
45. D. Zhang, J. He, Y. Liu, L. Si, and R. D. Lawrence. Multiview transfer learning with a large margin approach. In KDD, pages 1208-1216, 2011.
46. D. Zhang, Y. Liu, L. Si, J. Zhang, and R. D. Lawrence. Multiple instance learning on structured data. In NIPS, pages 145- 153, 2011.
47. D. Zhang, F. Wang, C. Zhang, and T. Li. Multi-view local learning. In AAAI, pages 752-757, 2008.
48. M.-L. Zhang and Z.-H. Zhou. M3miml: A maximum margin method for multi-instance multi-label learning. In ICDM, pages 688-697, 2008.
49. Q. Zhang and S. A. Goldman. Em-dd: An improved multipleinstance learning technique. In NIPS, pages 1073-1080, 2001.
50. T. Zhang, A. Popescul, and B. Dom. Linear prediction models with graph regularization for web-page categorization. In SIGKDD, pages 821-826, 2006.
51. Z.-H. Zhou and M.-L. Zhang. Multi-instance multi-label learning with application to scene classification. In NIPS, pages 1609-1616, 2006.