Integrating distance metrics learned from multiple experts and its application in patient similarity assessment

Proceedings of the 11th SIAM International Conference on Data Mining, SDM 2011

Volumn , Issue , 2011, Pages 59-70

  Wang, Fei ^a   Sun, Jimeng ^a   Ebadollahi, Shahram ^a  

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

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); DECISION SUPPORT SYSTEMS; HOSPITAL DATA PROCESSING; QUADRATIC PROGRAMMING;

CLASSIFICATION ACCURACY; CLINICAL DECISION SUPPORT; COMPARATIVE EFFECTIVENESS; DISTANCE INTEGRATION; OPTIMAL SOLUTIONS; PRIVACY PRESERVING; QUADRATIC OPTIMIZATION PROBLEMS; SIMILARITY ASSESSMENT;

DATA MINING;

EID: 84867231327     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1137/1.9781611972818.6     Document Type: Conference Paper

References (31)

1. C. Aggarwal and P. S. Yu. Privacy-Preserving Data Mining: Models and Algorithms. Springer, 2008.
2. F. R. Bach, G. R. G. Lanckriet, and M. I. Jordan. Multiple kernel learning, conic duality, and the smo algorithm. In Proc. of International Conference on Machine Learning, pages 6-13, 2004.
3. L. Breiman. Bagging predictors. Machine Learning, 24(2): 123-140, 1996.
4. D. Cai, X. He, K. Zhou, J. Han, and H. Bao. Locality sensitive discriminant analysis. In Proceedings of the 20th International Joint Conference on Artifical Intelligence, pages 708-713, 2007.
5. T. F. Cox and M. A. A. Cox. Multimensional Scaling. London, U.K., 2001.
6. J. Duchi, S. Shalev-Shwartz, Y. Singer, and T. Chandra. Efficient projections onto the 11-ball for learning in high dimensions. In Proceedings of the 25th international conference on Machine learning, pages 272-279, 2008.
7. R. O. Duda, P. E. Hart, and D. H. Stork. Pattern Classification (2nd ed.). Wiley Interscience, 2000.
8. T. Fawcett. An introduction to roc analysis. Pattern Recognition Letters, 27:861-874, 2006.
9. Y Freund and R. E. Schapire. A decision-theoretic generalization of on-line learning and an application to boosting. In European Conference on Computational Learning Theory, pages 23-37, 1995.
10. J. H. Friedman. Regularized discriminant analysis. Journal of the American Statistical Association, 84(405): 165-175, 1989.
11. J. Goldberger, S. Roweis, G. Hinton, and R. Salakhutdinov. Neighbourhood component analysis. In Advances in Neural Information Processing Systems 17, pages 513-520, 2005.
12. G. H. Golub and C. F. V. Loan. Matrix Computation (3rd ed.). The Johns Hopkins University Press, 1996.
13. D. J. Hand. Measuring classifier performance: A coherent alternative to the area under the roc curve. Machine Learning, 77:103-123, 2009.
14. G. Hinton and S. Roweis. Stochastic neighbor embedding. In Advances in Neural Information Processing Systems 15, pages 833-840. MIT Press, 2002.
15. A. E. Hoerl and R. Kennard. Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12:55-67, 1970.
16. I. Jolliffe. Principal Component Analysis (2nd ed.). Springer Verlag, Berlin, Germany, 2002.
17. G. R. G. Lanckriet, N. Cristianini, P. Bartlett, L. El Ghaoui, and M. I. Jordan. Learning the kernel matrix with semidefinite programming. J. Mach. Learn. Res., 5:27-72, 2004.
18. J. Liu and J. Ye. Efficient euclidean projections in linear time. In ICML.
19. B. Schölkopf and A. J. Smola. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT press, Cambridge, MA, 2002.
20. S. Sonnenburg, G. Ratsch, C. Schäfer, and B. Schölkopf. Large Scale Multiple Kernel Learning. Journal of Machine Learning Research, 7:1531-1565, July 2006.
21. J. Sun, D. Sow, J. Hu, and S. Ebadollahi. Localized supervised metric learning on temporal physiological data. In ICPR.
22. J. Vaidya, C. Clifton, and M. Zhu. Privacy preserving data mining. Springer, 2005.
23. V. Vapnik. The Nature of Statistical Learning Theory. Springer-Verlag, 1995.
24. F. Wang, S. Chen, T. Li, and C. Zhang. Semi-supervised metric learning by maximizing constraint margin. In Proceedings of ACM 17th Conference onlnformation and Knowledge Management, pages 1457-1458, 2008.
25. F. Wang, J. Sun, T. Li, and N. Anerousis. Two heads better than one: Metric+active learning and its applications for it service classification. In IEEE International Conference on Data Mining, pages 1022-1027, 2009.
26. K. Q. Weinberger and L. K. Saul. Distance metric learning for large margin nearest neighbor classification. The Journal of Machine Learning Research, 10:207-244, 2009.
27. E. Xing, A. Ng, M. Jordan, and S. Russell. Distance metric learning with application to clustering with side-information. In Advances in Neural Information Processing System 15, pages 505-512, 2003.
28. L. Yang. Distance metric learning: A comprehensive survey. Technical report, Department of Computer Science and Engineering, Michigan State University, 2006.
29. J. Ye and T. Xiong. Computational and theoretical analysis of null space and orthogonal linear discriminant analysis, volume 7, pages 1183-1204, 2006.
30. H. Zha, X. He, C. Ding, H. Simon, and M. Gu. Spectral relaxation for k-means clustering. In Advances in Neural Information Processing Systems, pages 1057-1064, 2001.
31. H. Zou and T. Trevor. Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society, Series B, 67(2):301-320, 2005.