Maximum margin multiple instance clustering with applications to image and text clustering

IEEE Transactions on Neural Networks

Volumn 22, Issue 5, 2011, Pages 739-751

  Zhang, Dan ^a   Wang, Fei ^b   Si, Luo ^a   Li, Tao ^c  

^a Purdue University   (United States)

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

^c FLORIDA INTERNATIONAL UNIVERSITY   (United States)

Author keywords

Constrained concave convex procedure; cutting plane; maximum margin; multiple instance clustering

Indexed keywords

CONCAVE-CONVEX PROCEDURE; CUTTING PLANE; CUTTING PLANE METHODS; EMPIRICAL RESULTS; MAXIMUM MARGIN; MULTIPLE INSTANCE CLUSTERING; MULTIPLE INSTANCE LEARNING; MULTIPLE INSTANCES; OPTIMIZATION PROBLEMS; OPTIMIZATION SOLUTION; TEXT CLUSTERING;

ENGINEERING RESEARCH;

CONSTRAINED OPTIMIZATION;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; AUTOMATED PATTERN RECOGNITION; COMPUTER PROGRAM; COMPUTER SIMULATION; IMAGE PROCESSING; MATHEMATICAL PHENOMENA; METHODOLOGY; STANDARD;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; IMAGE PROCESSING, COMPUTER-ASSISTED; MATHEMATICAL CONCEPTS; NEURAL NETWORKS (COMPUTER); PATTERN RECOGNITION, AUTOMATED; SOFTWARE DESIGN;

EID: 79955835564     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNN.2011.2109011     Document Type: Article

References (39)

1. S. Andrews, I. Tsochantaridis, and T. Hofmann, "Support vector machines for multiple-instance learning," in Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press, 2003, pp. 561-568.
2. T. G. Dietterich, R. H. Lathrop, and T. Lozano-Pérez, "Solving the multiple instance problem with axis-parallel rectangles," Artif. Intell., vol. 89, nos. 1-2, pp. 31-71, Jan. 1998.
3. R. Rahmani and S. A. Goldman, "MISSL: Multiple-instance semisupervised learning," in Proc. Int. Conf. Mach. Learn., vol. 10. 2006, pp. 705-712.
4. R. Amar, D. R. Dooly, S. A. Goldman, and Q. Zhang, "Multiple- instance learning of real-valued data," in Proc. 18th Int. Conf. Mach. Learn., 2001, pp. 3-10.
5. S. Ray and D. Page, "Multiple instance regression," in Proc. Int. Conf. Mach. Learn., 2001, pp. 425-432.
6. Q. Zhang and S. A. Goldman, "EM-DD: An improved multiple-instance learning technique," in Proc. Adv. Neural Inf. Process. Syst., 1998, pp. 1073-1080.
7. A. K. Jain and R. C. Dubes, Algorithms for Clustering Data. Upper Saddle River, NJ: Prentice-Hall, 1988.
8. J. Han and M. Kamber, Data Mining. San Mateo, CA: Morgan Kaufmann, 2001.
9. C. Manning and P. Raghavan, Introduction to Information Retrieval. Cambridge, U.K.: Cambridge Univ. Press, 2008.
10. M. L. Zhang and Z. H. Zhou, "Multi-instance clustering with applications to multi-instance prediction," Appl. Intell., vol. 31, no. 1, pp. 47-68, Aug. 2009.
11. J. Wang and J. D. Zucker, "Solving the multiple-instance problem: A lazy learning approach," in Proc. Int. Conf. Mach. Learn., 2000, pp. 1119-1126.
12. D. Zhang, F. Wang, L. Si, and T. Li, "M3IC: Maximum margin multiple instance clustering," in Proc. 21st Int. Joint Conf. Artif. Intell., 2009, pp. 1339-1344.
13. O. Maron and T. Lozano-Pérez, "A framework for multiple-instance learning," in Proc. Adv. Neural Inf. Process. Syst., vol. 10. 1998, pp. 570-576.
14. O. Maron and A. L. Ratan, "Multiple-instance learning for natural scene classification," in Proc. Int. Conf. Mach. Learn., 1998, pp. 341-349.
15. R. Rahmani, S. A. Goldman, H. Zhang, J. Krettek, and J. E. Fritts, "Localized content based image retrieval," in Proc. Int. Workshop Multimedia Inf. Ret., 2005, pp. 227-236.
16. B. Scholkopf and A. Smola, Learning with Kernels. Cambridge, MA: MIT Press, 2002.
17. P. Gehler and O. Chapelle, "Deterministic annealing for multipleinstance learning," in Proc. 11th Int. Conf. Artif. Intell. Stat., 2007, pp. 123-130.
18. T. Gartner, P. A. Flach, A. Kowalczyk, and A. J. Smola, "Multi-instance kernels," in Proc. Int. Conf. Mach. Learn., 2002, pp. 179-186.
19. J. T. Kwok and P.-M. Cheung, "Marginalized multi-instance kernels," in Proc. 19th Int. Joint Conf. Artif. Intell., 2007, pp. 901-906.
20. C. B. Razvan and R. J. Mooney, "Multiple instance learning for sparse positive bags," in Proc. Int. Conf. Mach. Learn., 2007, pp. 112-119.
21. Y. Chen and J. Z.Wang, "Image categorization by learning and reasoning with regions," J. Mach. Learn. Res., vol. 5, pp. 913-939, Aug. 2004.
22. Y. Chen, J. Bi, and J. Z. Wang, "MILES: Multiple-instance learning via embedded instance selection," IEEE Trans. Pattern Anal. Mach. Intell., vol. 28, no. 12, pp. 1931-1947, Dec. 2006. (Pubitemid 46405705)
23. Q. Tao, D. Chu, and J. Wang, "Recursive support vector machines for dimensionality reduction," IEEE Trans. Neural Netw., vol. 19, no. 1, pp. 189-193, Jan. 2008. (Pubitemid 351279237)
24. L. Xu, J. Neufeld, B. Larson, and D. Schuurmans, "Maximum margin clustering," in Proc. Adv. Neural Inf. Process. Syst., 2005, pp. 1537-1544.
25. L. Xu and D. Schuurmans, "Unsupervised and semi-supervised multiclass support vector machines," in Proc. Nat. Conf. Artif. Intell., 2005, pp. 904-910. (Pubitemid 43006723)
26. K. Zhang, I. W. Tsang, and J. T. Kwok, "Maximum margin clustering made practical," IEEE Trans. Neural Netw., vol. 20, no. 4, pp. 583-596, Apr. 2009.
27. B. Zhao, F. Wang, and C. Zhang, "Efficient maximum margin clustering via cutting plane algorithm," in Proc. SIAM Int. Conf. Data Mining, 2008, pp. 751-762.
28. B. Zhao, F. Wang, and C. Zhang, "Efficient multiclass maximum margin clustering," in Proc. Int. Conf. Mach. Learn., 2008, pp. 751-762.
29. F. Wang, B. Zhao, and C. Zhang, "Linear time maximum margin clustering," IEEE Trans. Neural Netw., vol. 21, no. 2, pp. 319-332, Feb. 2010.
30. A. J. Smola, S. V. N. Vishwanathan, and T. Hofmann, "Kernel methods for missing variables," in Proc. Int. Workshop Artif. Intell. Stat., 2005, pp. 325-332.
31. T. Joachims, "Training linear SVMs in linear time," in Proc. Int. Conf. Knowl. Discovery Data Mining, 2006, pp. 217-226. (Pubitemid 44535518)
32. J. M. Borwein and A. S. Lewis, Convex Analysis and Nonlinear Optimization: Theory and Examples. New York: Springer-Verlag, 2006.
33. J. E. Kelley, "The cutting-plane method for solving convex programs," J. Soc. Ind. Appl. Math., vol. 8, no. 4, pp. 703-712, Dec. 1960.
34. B. Zhao, F. Wang, and C. Zhang, "Cuts3vm: A fast semi-supervised SVM algorithm," in Proc. 14th Int. Conf. Knowl. Discovery Data Mining, 2008, pp. 830-838.
35. S. P. Boyd and L. Vandenberghe, Convex Optimization. Cambridge, U.K.: Cambridge Univ. Press, 2004.
36. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification. New York: Wiley, 2001.
37. C. H. Papadimitriou and K. Steiglitz, Combinatorial Optimization: Algorithms and Complexity. New York: Dover, 1998.
38. A. Strehl and J. Ghosh, "Cluster ensembles-A knowledge reuse framework for combining multiple partitions," J. Mach. Learn. Res., vol. 3, pp. 583-617, Dec. 2002.
39. I. Tsochantaridis, T. Joachims, T. Hofmann, and Y. Altun, "Large margin methods for structured and interdependent output variables," J. Mach. Learn. Res., vol. 6, pp. 1453-1484, Sep. 2005.