Formulating context-dependent similarity functions

Proceedings of the 13th ACM International Conference on Multimedia, MM 2005

Volumn , Issue , 2005, Pages 725-734

  Wu, Gang ^a   Chang, Edward Y ^a   Panda, Navneet ^a  

^a Santa Barbara   (United States)

Author keywords

Function learning; Kernel machines

Indexed keywords

CONTEXTUAL INFORMATION; EFFECTIVE DISTANCE; FUNCTION LEARNING; KERNEL MACHINE; LEARNING PERFORMANCE; MEASURING SIMILARITIES; NON-LINEAR RELATIONSHIPS; SIMILARITY FUNCTIONS;

LEARNING SYSTEMS;

EID: 84883098050     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1101149.1101307     Document Type: Conference Paper

References (32)

1. C. Aggarwal, A. Hinneburg, and D. Keim. On the surprising behavior of distance metrics in high dimensional spaces. In Proceedings of International Conference on Database Theory, pages 420-434, 2001.
2. C. C. Aggarwal. Towards systematic design of distance functions for data mining applications. The Ninth ACM SIGKDD International Conference on Knowledge Discovery in Data and Data Mining, 2003.
3. M. A. Aizerman, E. M. Braverman, and L. I. Rozonoer. Theoretical foundations of the potential function method in pattern recognition learning. Automation and Remote Control, 25:821-837, 1964.
4. E. Amaldi and V. Kann. On the approximability of minimizing non-zero variables or unsatisfied relations in linear systems. Theoretical Computer Science, 209:237-260, 1998.
5. A. Bar-hillel, T. Hertz, N. Shental, and D. Weinshall. Learning distance functions using equivalence relations. In Proceedings of the Twentieth International Conference on Machine Learning, August 2003.
6. A. Ben-Hur, D. Horn, H. T. Siegelmann, and V. Vapnik. Support vector clustering. Journal of Machine Learning Research, 2:125-137, March 2002.
7. K. Beyer, J. Goldstein, R. Ramakrishnan, and U. Shaft. When is "nearest neighbor" meaningful? Lecture Notes in Computer Science, 1999.
8. th IEEE Conference on Emerging Information Technology (invited), August 2005.
9. N. Cristianini, J. Kandola, A. Elisseeff, and J. Shawe-Taylor. On kernel target alignment. Journal Machine Learning Research, 1, 2002.
10. R. Fagin, R. Kumar, and D. Sivakumar. Efficient similarity search and classification via rank aggregation. In Proceedings of ACM SIGMOD Conference on Management of Data, pages 301-312, June 2003.
11. M. Fazel. Matrix rank minimization with applications. Ph.D. Thesis, Electrical Engineering Dept, Stanford University, March 2002.
12. A. Gionis, P. Indyk, and R. Motwani. Similarity search in high dimensions via hashing. In Proceedings of the 25th VLDB Conference, pages 518-529, 1999.
13. Y. Grandvalet and S. Canu. Adaptive scaling for feature selection in svms. Advances in Neural Information Processing Systems 15, 2003.
14. R. Kanna, S. Vempala, and A. Vetta. On clusterings - good, bad and spectral. In FOGS, pages 367-377, 2000.
15. H. W. Kuhn and A. W. Tucker. Nonlinear programming. In Proceedings of the Second Berkeley Symposium on Mathematical Statistics and Probabilistics, pages 481-492, 1951.
16. J. T. Kwok and I. W. Tsang. Learning with idealized kernels. In Proceedings of the Twentieth International Conference on Machine Learning, pages 400-407, August 2003.
17. M. Meila and J. Shi. Learning segmentation by random walks. In Advances in Neural Information Processing Systems, pages 873-879, 2000.
18. M. Nadler and E. P. Smith. Pattern Recognition Engineering. New York: Wiley, 1993.
19. A. Y. Ng, M. I. Jordan, and Y. Weiss. On spectral clustering: Analysis and an algorithm. In Advances in Neural Information Processing Systems, pages 849-856, 2002.
20. J. Platt. Sequential minimal optimization: A fast algorithm for training support vector machines. Technical Report MSR-TR-98-14, Microsoft Research, 1998.
21. Y. Rui and T. Huang. Optimizing learning in image retrieval. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 236-245, June 2000.
22. B. Schölkopf and A. Smola. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge, MA, 2002.
23. J. Shi and J. Malik. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8):888-905, 2000.
24. S. Tong and E. Chang. Support vector machine active learning for image retrieval. In Proceedings of ACM International Conference on Multimedia, pages 107-118, 2001.
25. V. Vapnik. Statistical Learning Theroy. John Wiley and Sons, 1998.
26. T. Wang, Y. Rui, S. min Hu, and J. guang Sun. Adaptive tree similarity learning for image retrieval, 2003.
27. A. R. Webb. Multidimensional scaling by iterative majorization using radial basis functions. Pattern Recognition, 28(5):753-759, 1995.
28. D. Wettschereck, D. Aha, and T. Mohri. A review and empirical evaluation of feature weighting methods for a class of lazy learning algorithms. Artificial Intelligence Review, 11:273-314, 1997.
29. G. Wu, N. Panda, and E. Chang. Formulating distance functions via the kernel trick. ACM International Conference on Knowledge Discovery and Data Mining (poster), August 2005.
30. G. Wu, A. Rahimi, K. Goh, C. Tsai, Y. Wu, E. Chang, and Y.-F. Wang. Identifying color in motion in video sensors. UCSB Technical Report, 2005.
31. E. Xing, A. Ng, M. Jordan, and S. Russell. Distance metric learning, with application to clustering with side-information. Advances in Neural Information Processing Systems 15, 2003.
32. Z. Zhang. Learning metrics via discriminant kernels and multidimensional scaling: Towards expected euclidean representation. In Proceedings of the Twentieth International Conference on Machine Learning, August 2003.