A boosting approach for supervised Mahalanobis distance metric learning

Pattern Recognition

Volumn 45, Issue 2, 2012, Pages 844-862

  Chang, Chin Chun ^a  

^a NATIONAL TAIWAN OCEAN UNIVERSITY   (Taiwan)

Author keywords

Boosting approaches; Distance metric learning; Hypothesis margins

Indexed keywords

BOOSTING ALGORITHM; BOOSTING APPROACH; DATA SETS; DISTANCE METRIC LEARNING; DISTANCE METRICS; HYPOTHESIS MARGINS; LOSS FUNCTIONS; MAHALANOBIS DISTANCES; METRIC MATRIX;

ALGORITHMS;

ADAPTIVE BOOSTING;

EID: 80052962748     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2011.07.026     Document Type: Article

References (49)

1. L. Yang, R. Jin, Distance Metric Learning: A Comprehensive Survey, Technical Report 24, Department of Computer Science and Engineering, Michigan State University, 2006.
2. R.A. Fisher The use of multiple measurements in taxonomic problems Annals of Eugenics 7 1936 179 188
3. A. Bar-Hillel, T. Hertz, N. Shental, and D. Weinshall Learning a Mahalanobis metric from equivalence constraints Journal of Machine Learning Research 6 2005 937 965
4. M. Loog, and R.P.W. Duin Linear dimensionality reduction via a heteroscedastic extension of LDA: the Chernoff criterion IEEE Transactions on Pattern Analysis and Machine Intelligence 26 6 2004 732 739
5. D. Xu, S. Yan, D. Tao, S. Lin, and H.J. Zhang Marginal Fisher analysis and its variants for human gait recognition and content-based image retrieval IEEE Transactions on Image Processing 16 11 2007 2811 2821 (Pubitemid 350141853)
6. M. Sugiyama Dimensionality reduction of multimodal labeled data by local Fisher discriminant analysis Journal of Machine Learning Research 8 2007 1027 1061 (Pubitemid 46798409)
7. K. Fukunaga, and J.M. Mantock Nonparametric discriminant analysis IEEE Transactions on Pattern Analysis and Machine Intelligence 5 1983 671 678
8. F. Wang, and C. Zhang Feature extraction by maximizing the average neighborhood margin Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2007 1 8
9. T. Hastie, and R. Tibshirani Discriminant adaptive nearest neighbor classification IEEE Transactions on Pattern Analysis and Machine Intelligence 18 6 1996 607 616 (Pubitemid 126773896)
10. K. Fukunaga, and T.E. Flick An optimal global nearest neighbor metric IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-6 3 1984 314 318 (Pubitemid 14563808)
11. S. Yan, D. Xu, B. Zhang, H.J. Zhang, Q. Yang, and S. Lin Graph embedding and extensions: a general framework for dimensionality reduction IEEE Transactions on Pattern Analysis and Machine Intelligence 29 1 2007 40 51 (Pubitemid 46415944)
12. E.P. Xing, A.Y. Ng, M.I. Jordan, and S. Russell Distance metric learning with application to clustering with side-information S.T.S. Becker, K. Obermayer, Advances in Neural Information Processing Systems vol. 15 2003 MIT Press Cambridge, MA 505 512
13. S. Yan, J. Liu, X. Tang, and T.S. Huang A parameter-free framework for general supervised subspace learning IEEE Transactions on Information Forensics and Security 2 1 2007 69 76
14. K.Q. Weinberger, and L.K. Saul Distance metric learning for large margin nearest neighbor classification Journal of Machine Learning Research 10 2009 207 244
15. R. Jin, and S. Wang Regularized distance metric learning: theory and algorithm Advance in Neural Information Processing Systems vol. NIPS 23 2009
16. F. Wang, S. Chen, T. Li, and C. Zhang Semi-supervised metric learning by maximizing constraint margin Proceedings of ACM 17th Conference on Information and Knowledge Management (CIKM) 2008 1457 1458
17. L. Yang, R. Jin, R. Sukthankar, and Y. Liu An efficient algorithm for local distance metric learning Proceedings of the Twenty-first National Conference on Artificial Intelligence 2006
18. J. Goldberger, S. Roweis, G. Hinton, and R. Salakhutdinov Neighbourhood components analysis L.K. Saul, Y. Weiss, L. Bottou, Advances in Neural Information Processing Systems vol. 17 2005 MIT Press Cambridge, MA 513 520
19. Y. Sun Iterative RELIEF for feature weighting: algorithms, theories and applications IEEE Transactions on Pattern Analysis and Machine Intelligence 29 6 2007 1035 1051 (Pubitemid 46667414)
20. Y. Sun, S. Todorovic, and S. Goodison Local-learning-based feature selection for high-dimensional data analysis IEEE Transactions on Pattern Analysis and Machine Intelligence 32 9 2010 1610 1626
21. J. Friedman, T. Hastie, and R. Tibshirani Additive logistic regression: a statistical view of boosting Annals of Statistics 28 2000 337 407 (Pubitemid 33227445)
22. R. Meir, and G. Rätsch An introduction to boosting and leveraging Advanced Lectures on Machine Learning, Lecture Notes in Computer Science 2003 Springer 119 184
23. R.E. Schapire The boosting approach to machine learning: an overview Workshop on Nonlinear Estimation and Classification, MSRI 2002
24. Y. Freund, and R.E. Schapire A decision-theoretic generalization of on-line learning and an application to boosting Journal of Computer and System Science 55 1 1997 119 139 (Pubitemid 127433398)
25. R.E. Schapire, and Y. Singer Improved boosting algorithms using confidence-rated predictions Machine Learning 37 3 1999 297 336 (Pubitemid 32210620)
26. T. Hertz, A. Bar-Hillel, and D. Weinshall Boosting margin based distance functions for clustering Proceedings of the Twenty-First International Conference on Machine Learning 2004 393 400 (Pubitemid 40290833)
27. T. Hertz, A. Bar-Hillel, and D. Weinshall Learning a kernel function for classification with small training samples ICML 06: Proceedings of the 23rd International Conference on Machine Learning 2006 ACM New York, NY, USA 401 408
28. L. Yang, R. Jin, L. Mummert, R. Sukthankar, A. Goode, B. Zheng, S.C.H. Hoi, and M. Satyanarayanan A boosting framework for visuality-preserving distance metric learning and its application to medical image retrieval IEEE Transactions on Pattern Analysis and Machine Intelligence 32 1 2010 30 44
29. K. Crammer, J. Keshet, and Y. Singer Kernel design using boosting Advances in Neural Information Processing Systems vol. 15 2003 MIT Press 537 544
30. K. Crammer, R. Gilad-bachrach, A. Navot, and N. Tishby Margin analysis of the LVQ algorithm Advances in Neural Information Processing Systems vol. 14 2002 MIT Press 462 469
31. R. Gilad-Bachrach, A. Navot, and N. Tishby Margin based feature selection - theory and algorithms International Conference on Machine Learning (ICML) 2004 ACM Press 43 50
32. R. Kohavi, and G. John Wrappers for feature subset selection Artificial Intelligence 97 1997 273 324 (Pubitemid 127401107)
33. C.C. Chang Generalized iterative RELIEF for supervised distance metric learning Pattern Recognition 43 2010 2971 2981
34. T.M. Cover, and J.A. Thomas Elements of Information Theory 1991 John Wiley & Sons, Inc. New York
35. A.P. Dempster, N.M. Laird, and D.B. Rubin Maximum likelihood from incomplete data via the EM algorithm Journal of the Royal Statistical Society Series B 39 1 1977 1 38
36. F.R.K. Chung, Spectral Graph Theory, American Mathematical Society, 1997.
37. D. Hush, C. Scovel, and I. Steinwart Stability of unstable learning algorithm Machine Learning 67 2007 197 206
38. T. Zhang Covering number bounds of certain regularized linear function classes Journal of Machine Learning Research 2 2002 527 550 (Pubitemid 135712571)
39. O. Bousquet, and A. Elisseeff Stability and generalization Journal of Machine Learning Research 2 2002 499 526 (Pubitemid 135712570)
40. A. Asuncion, D. Newman, UCI Machine Learning Repository, 〈 http://www.ics.uci.edu/∼mlearn/MLRepository.html 〉, 2007.
41. U. Alon, N. Barkai, D.A. Notterman, K. Gish, S. Ybarra, D. Mack, and A.J. Levine Broad patterns of gene expression revealed by clustering of tumor and normal colon tissues probed by oligonucleotide arrays Proceedings of National Academy of Science USA 96 12 1999 6745 6750 (Pubitemid 29274954)
42. D. Singh, P.G. Febbo, K. Ross, D.G. Jackson, J. Manola, C. Ladd, P. Tamayo, A.A. Renshaw, A.V. DAmico, J.P. Richie, E.S. Lander, M. Loda, P.W. Kantoff, T.R. Golub, and W.R. Sellers Gene expression correlates of clinical prostate cancer behavior Cancer Cell 1 2002 203 209 (Pubitemid 41039166)
43. S. Ramaswamy, P. Tamayo, R. Rifkin, S. Mukherjee, C. Yeang, M. Angelo, C. Ladd, M. Reich, E. Latulippe, J. Mesirov, T. Poggio, W. Gerald, M. Loda, E. Lander, and R. Golub Multiclass cancer diagnosis using tumor gene expression signatures Proceedings of National Academy of Science USA 98 26 2001 15149 15154 (Pubitemid 34013984)
44. Georgia Tech Face Database, 〈 http://www.anefian.com/face-reco.htm 〉.
45. K. Lee, J. Ho, and D. Kriegman Acquiring linear subspaces for face recognition under variable lighting IEEE Transactions on Pattern Analysis and Machine Intelligence 27 5 2005 684 698 (Pubitemid 40608402)
46. A.M. Martinez, R. Benavente, The AR Face Database, Technical Report 24, CVC, June 1998.
47. T. Hastie, R. Tibshirani, and J. Friedman The Elements of Statistical Learning: Data Mining, Inference, and Prediction 2001 Springer
48. R.O. Duda, P.E. Hart, and D.G. Stork Pattern Classification second ed. 2001 John Wiley & Sons, Inc.
49. K. Fukunaga Introduction to Statistical Pattern Recognition second ed. 1990 Academic Press Boston