Non-i.i.d. multi-instance dimensionality reduction by learning a maximum bag margin subspace

Proceedings of the National Conference on Artificial Intelligence

Volumn 1, Issue , 2010, Pages 551-556

  Ping, Wei ^a,b   Xu, Ye ^c   Ren, Kexin ^d   Chi, Chi Hung ^b   Shen, Furao ^c  

^a TSINGHUA NATIONAL LABORATORY FOR INFORMATION SCIENCE AND TECHNOLOGY   (China)

^b TSINGHUA UNIVERSITY   (China)

^c NANJING UNIVERSITY   (China)

^d NANJING UNIVERSITY OF AERONAUTICS AND ASTRONAUTICS   (China)

Author keywords

[No Author keywords available]

Indexed keywords

LEARNING SYSTEMS;

CURSE OF DIMENSIONALITY; DIMENSIONALITY REDUCTION; DIMENSIONALITY REDUCTION METHOD; INDEPENDENTLY AND IDENTICALLY DISTRIBUTED; LABEL INFORMATION; MULTI-INSTANCE LEARNING; OBJECTIVE FUNCTIONS; STRUCTURE INFORMATION;

ARTIFICIAL INTELLIGENCE;

EID: 77958553299     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (26)

1. Andrews, S.; Tsochantaridis, I.; and Hofmann, T. 2003. Support vector machines for multiple-instance learning. In Advances in Neural Information Processing Systems, 561-568.
2. Bartelmaos, S., and Abed-Meraim, K. 2008. Fast principal component extraction using givens rotations. IEEE Signal Processing Letters 15:369-372.
3. Chen, Y., and Wang, J. Z. 2004. Image categorization by learning and reasoning with regions. Journal of Machine Learning Research 5:913-939.
4. Devijver, P. A., and Kittler, J. 1982. Pattern Recognition: A Statistical Approach. Prince-Hall.
5. Dietterich, T. G.; Lathrop, R. H.; and Lozano-Perez, T. 1997. Solving the multiple-instance problem with axis-parallel retangles. 89(1):31-71.
6. Fisher, R. A. 1936. The use of multiple measurements in taxonomie problems. Annals of Eugenics 7(2): 179-188.
7. Friedman, J. 1989. Regularized discriminant analysis. Journal of American Statistical Association 84(405):165-175.
8. Fung, G.; Dundar, M.; Krishnappuram, B.; and Rao, R. B. 2007. Multiple instance learning for computer aided diagnosis. In Advances in Neural Information Processing Systems, 425-432.
9. Gartner, T.; Flach, P. A.; Kowalczyk, A.; and Smola, A. 2002. Multi-instance kernels. In Proceedings of the International Conference on Machine Learning, 179-186.
10. He, X., and Niyogi, P. 2003. Locality preserving projections. In Advances in Neural Information Processing Systems.
11. Howland, P.; Jeon, M.; and Park, H. 2003. Structure preserving dimension reduction for clustered text data based on the generalized singular value decomposition. SIAM Journal of Matrix Analysis and Applications 25(1): 165-179.
12. Li, H. F, and Jiang, T. 2003. Efficient and robust feature extraction by maximum margin criterion. In Advances in Neural Information Processing Systems, 97-104.
13. Maron, O., and Lozano-Perez, T. 1998. A framework for multiple-instance learning. In Advances in Neural Informa-tion Processing Systems, 570-576.
14. Qi, G.-J.; Hua, X.-S.; Rui, Y.; Mei, T.; Tang, J.; and Zhang, H.-J. 2007. Concurrent multiple instance learning for image categorization. In IEEE Conference on Computer Vision and Pattern Recognition, 1-8.
15. Roweis, S. T., and Saul, L. K. 2000. Nonlinear dimensionality reduction by locally linear embedding. Science 290:2323-2326.
16. Settles, B.; Graven, M.; and Ray, S. 2008. Multiple-instance active learning. In Advances in Neural Information Processing Systems, 1289-1296.
17. Tenenbaum, J. B.; Silva, V.; and Langford, J. C. 2000. A global geometric framework for nonlinear dimensionality reduction. Science 290:2319-2323.
18. Wang, J., and Zucker, J.-D. 2000. Solving the multiple-instance problem: a lazy learning approach. In Proceedings of the International Conference on Machine Learning, 1119-1125.
19. Weng, J.; Zhang, Y; and Hwang, W S. 2003. Candid covariance-free incremental principal component analysis. IEEE Trans. Pattern Analysis and Machine Intelligence 25(8): 1034-104O.
20. Xu, Y; Shen, R; Zhao, J.; and Hasegawa, O. 2009. To obtain orthogonal feature extraction using training data selection. In ACM Conference on Information and Knowledge Management, 1819-1822.
21. Yan, J.; Zhang, B.; Yan, S.; Yang, Q.; Li, H.; Chen, Z.; Xi, W; Fan, W; Ma, W.-Y; and Cheng, Q. 2004. Immc: incremental maximum margin criterion. In ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 725-73O.
22. Ye, J.; Janardan, R.; Park, C; and Park, H. 2004. An op-timization criterion for generalized discriminant analysis on undersampled problems. IEEE Trans. Pattern Analysis and Machine Intelligence 26(8):982-994.
23. Zhang, Y, and Yeung, D.-Y. 2008. Semi-supervised discriminant analysis via cccp. In Proceedings of the European Conference on Machine Learning, 644-659.
24. Zhou, Z.-H., and Xu, J.-M. 2007. On the relation between multi-instance learning and semi-supervised learning. In Proceedings of the International Conference on Machine Learning, 1167-1174.
25. Zhou, Z.-H., and Zhang, M.-L. 2003. Ensembles of multi-instance learners. In Proceedings of the European Conference on Machine Learning, 492-502.
26. Zhou, Z.-H.; Sun, Y-Y; and Li, Y.-F 2009. Multi-instance learning by treating instances as non-i.i.d. samples. In Proceedings of the International Conference on Machine Learning, 1249-1256.