Maximum margin multiple-instance feature weighting

Pattern Recognition

Volumn 47, Issue 6, 2014, Pages 2091-2103

  Chai, Jing ^a   Chen, Hongtao ^a   Huang, Lixia ^a   Shang, Fanhua ^b  

^a TAIYUAN UNIVERSITY OF TECHNOLOGY   (China)

^b XIDIAN UNIVERSITY   (China)

Author keywords

Coordinate ascent; Feature weighting; Maximum margin; Multiple instance learning

Indexed keywords

EID: 84894321541     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2013.12.009     Document Type: Article

References (48)

1. T.G. Dietterich, R.H. Lathrop, and T. Lozano-Perez Solving the multiple instance problem with axis-parallel rectangles Artif. Intell. 89 1 1997 31 71
2. O. Maron, A.L. Ratan, Multiple-instance learning for natural scene classification, in: Proceedings of the 15th International Conference on Machine Learning, 1998, pp. 341-349.
3. Y. Chen, J. Bi, and J.Z. Wang MILES: multiple-instance learning via embedded instance selection IEEE Trans. Pattern Anal. Mach. Intell. 28 12 2006 1931 1947
4. Q. Zhang, S.A. Goldman, W. Yu, J. Fritts, Content-based image retrieval using multiple-instance learning, in: Proceedings of the 19th International Conference on Machine Learning, 2002, pp. 682-689.
5. W.J. Li, and D.Y. Yeung MILD: multiple-instance learning via disambiguation IEEE Trans. Knowl. Data Eng. 22 1 2010 76 89
6. S. Andrews, I. Tsochantaridis, and T. Hofmann Support vector machines for multiple-instance learning Adv. Neural Inf. Process. Syst. 2003 561 568
7. S. Ray, M. Craven, Supervised versus multiple instance learning: an empirical comparison, in: Proceedings of the 22nd International Conference on Machine Learning, 2005, pp. 697-704.
8. J. Bi, J. Liang, Multiple instance learning of pulmonary embolism detection with geodesic distance along vascular structure, in: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2007. pp. 1-8.
9. G. Fung, M. Dundar, B. Krishnapuram, and R.B. Rao Multiple instance learning for computer aided diagnosis Adv. Neural Inf. Process. Syst. 2007 425 432
10. G. Ruffo, Learning Single and Multiple Decision Trees for Security Applications (Ph.D. thesis), University of Turin, Italy, 2000.
11. Z.H. Zhou, Y.Y. Sun, Y.F. Li, Multi-instance learning by treating instances as non-i.i.d. samples, in: Proceedings of the 26th International Conference on Machine Learning, 2009, pp. 1249-1256.
12. Z.H. Zhou, and M.L. Zhang Multi-instance multi-label learning with application to scene classification Adv. Neural Inf. Process. Syst. 2006 1609 1616
13. Q. Zhang, and S.A. Goldman EM-DD: an improved multiple-instance learning technique Adv. Neural Inf. Process. Syst. 2002 1073 1080
14. J. Wang, J.D. Zucker, Solving the multiple-instance problem: a lazy learning approach, in: Proceedings of the 17th International Conference on Machine Learning, 2000, pp. 1119-1125.
15. T. Gartner, P.A. Flach, A. Kowalczyk, A.J. Smola, Multi-instance kernels, in: Proceedings of the 19th International Conference on Machine Learning, 2002, pp. 179-186.
16. C. Leistner, A. Saffari, H. Bischof, MIForests: multiple-instance learning with randomized trees, in: Proceedings of European Conference on Computer Vision, 2010, pp. 29-42.
17. V.C. Raykar, B. Krishnapuram, J. Bi, M. Dundar, R.B. Rao, Bayesian multiple instance learning: automatic feature selection and inductive transfer, in: Proceedings of the 25th International Conference on Machine Learning, 2008, pp. 808-815.
18. Y.Y. Sun, M.K. Ng, Z.H. Zhou, Multi-instance dimensionality reduction, in: Proceedings of the 24th AAAI Conference on Artificial Intelligence,2010, pp. 587-592.
19. Y. Li, D.M. Tax, R.P. Duin, and M. Loog Multiple-instance learning as a classifier combining problem Pattern Recognit. 46 3 2013 865 874
20. K. Kira, L.A. Rendell, A practical approach to feature selection, in: Proceedings of the 9th International Conference on Machine Learning, 1992, pp. 249-256.
21. Y.J. Sun, J. Li, Iterative RELIEF for feature weighting, in: Proceedings of the 23rd International Conference on Machine Learning, 2006, pp. 913-920.
22. Y.J. Sun Iterative RELIEF for feature weighting: algorithms, theories, and applications IEEE Trans. Pattern Anal. Mach. Intell. 29 6 2007 1035 1051
23. C.J. Veenman, and D.M.J. Tax LESS: a model-based classifier for sparse subspaces IEEE Trans. Pattern Anal. Mach. Intell. 27 9 2005 1496 1500
24. B. Chen, H. Liu, J. Chai, and Z. Bao Large margin feature weighting method via linear programming IEEE Trans. Knowl. Data Eng. 21 10 2009 1475 1488
25. I. Guyon Feature Extraction: Foundations and Applications vol. 207 2006 Springer
26. H. Liu, and M. Hiroshi Feature Selection for Knowledge Discovery and Data Mining 1998 Springer
27. I. Guyon, and E. André An introduction to variable and feature selection J. Mach. Learn. Res. 3 2003 1157 1182
28. R.O. Duda, P.E. Hart, and D.G. Stork Pattern Classification 2nd edition 2000 John Wiley & Sons
29. H.F. Li, T. Jiang, and K.S. Zhang Efficient and robust feature extraction by maximum margin criterion IEEE Trans. Neural Netw. 17 1 2006 157 165
30. I. Guyon, J. Weston, S. Barnhill, and V. Vapnik Gene selection for cancer classification using support vector machines Mach. Learn. 46 1-3 2002 389 422
31. K. Kira, L.A. Rendell, A practical approach to feature selection, in: Proceedings of the 9th International Conference on Machine Learning, 1992, pp. 249-256.
32. R. Kohavi, and G. John Wrappers for feature subset selection Artif. Intell. 97 1 1997 273 324
33. S. Kim, S. Choi, Local dimensionality reduction for multiple instance learning, in: IEEE International Workshop on Machine Learning for Signal Processing, 2010, pp. 13-29.
34. Y. Chen, and J.Z. Wang Image categorization by learning and reasoning with regions J. Mach. Learn. Res. 5 2004 913 939
35. Z.H. Zhou, J.M. Xu, On the relation between multi-instance learning and semi-supervised learning, in: Proceedings of the 24th International Conference on Machine Learning, 2007, pp. 1167-1174.
36. G. Csurka, C. Bray, C. Dance, L. Fan, Visual categorization with bags of keypoints, in: ECCV Workshop on Statistical Learning in Computer Vision, 2004, pp. 59-74.
37. A. Zafra, M. Pechenizkiy, and S. Ventura ReliefF-MI: an extension of ReliefF to multiple instance learning Neurocomputing 75 1 2012 210 218
38. M. Sugiyama Dimensionality reduction of multimodal labeled data by local Fisher discriminant analysis J. Mach. Learn. Res. 8 2007 1027 1061
39. B. Settles, M. Craven, and S. Ray Multiple-instance active learning Adv. Neural Inf. Process. Syst. 2008 1289 1296
40. K.Q. Weinberger, J. Blitzer, and L.K. Saul Distance metric learning for large margin nearest neighbor classification Adv. Neural Inf. Process. Syst. 2006 1473 1480
41. C. Shen, J. Kim, and L. Wang Scalable large-margin Mahalanobis distance metric learning IEEE Trans. Neural Netw. 21 9 2010 1524 1530
42. Z.H. Zhou, and M.L. Zhang Solving multi-instance problems with classifier ensemble based on constructive clustering Knowl. Inf. Syst. 11 2 2007 155 170
43. Y.F. Li, J.H. Hu, Y. Jiang, Z.H. Zhou, Towards discovering what patterns trigger what labels, in: Proceedings of the 26th AAAI Conference on Artificial Intelligence, 2012, pp. 1012-1018.
44. S. Boyd, and L. Vandenberghe Convex Optimization, Cambridge 2004 Cambridge University Press UK
45. W.H. Press, S.A. Teukolsky, W.T. Vetterling, and B.P. Flannery Numerical Recipes in C: The Art of Scientific Computing, Cambridge 1992 Cambridge University Press UK
46. F. Wilcoxon Individual comparisons by ranking methods Biometrics Bull. 1 6 1945 80 83
47. T.M. Mitchell Machine Learning 1997 McGraw Hill
48. J. Demšar Statistical comparisons of classifiers over multiple data sets J. Mach. Learn. Res. 7 2006 1 30