Semi-supervised metric learning by kernel matrix adaptation

2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005

Volumn , Issue , 2005, Pages 3210-3215

  Chang, Hong ^a   Yeung, Dit Yan ^a  

^a HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

Author keywords

Clustering; Kernel learning; Metric learning; Semi supervised learning

Indexed keywords

CLUSTERING; KERNEL LEARNING; METRIC LEARNING;

MATRIX ALGEBRA; METRIC SYSTEM; PROBLEM SOLVING; SET THEORY;

LEARNING SYSTEMS;

EID: 28444483973     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. K. Fukunaga and L. Hosteller. Optimization of k-nearest neighbor density estimates. IEEE Transactions on Information Theory, 19(3):320-326, 1973.
2. J.H. Friedman. Flexible metric nearest neighbor classification. Technical report, Department of Statistics, Stanford University, Stanford, CA, USA, November 1994.
3. T. Hastie and R. Tibshirani. Discriminant adaptive nearest neighbor classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(6):607-616, 1996.
4. D.G. Lowe. Similarity metric learning for a variable-kernel classifier. Neural Computation, 7(1):72-85, 1995.
5. J. Peng, D.R. Heisterkamp, and H.K. Dai. Adaptive kernel metric nearest neighbor classification. In Proceedings of the Sixteenth International Conference on Pattern Recognition, volume 3, pages 33-36, 2002.
6. T. Poggio and F. Girosi. Networks for approximation and learning. Proceedings of the IEEE, 78(9):1481-1497, 1990.
7. S. Basu, A. Banerjee, and R. Mooney. Semisupervised clustering by seeding. In Proceedings of the Nineteenth International Conference on Machine Learning, pages 19-26, 2002.
8. J. Sinkkonen and S. Kaski. Clustering based on conditional distributions in an auxiliary space. Neural Computation, 14(1):217-239, 2001
9. Z. Zhang, J.T. Kwok, and D.Y. Yeung. Parametric distance metric learning with label information. In Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence, pages 1450-1452, 2003.
10. K. Wagstaff and C. Cardie. Clustering with instance-level constraints. [n Proceedings of the Seventeenth International Conference on Machine Learning, pages 1103-1110, 2000.
11. K. Wagstaff, C. Cardie, S. Rogers, and S. Schroedl. Constrained k-means clustering with backgroud knowledge. In Proceedings of the Eighteenth International Conference on Machine Learning, pages 577-584, 2001.
12. Z. Lu and T. Leen. Penalized probabilistic clustering. In Advances in Neural Information Processing Systems 17. MIT Press, Cambridge, MA, USA, 2005.
13. N. Shental, A. Bar-Hillel, T. Hertz, and D. Weinshall. Computing Gaussian mixture models with EM using equivalence constraints. In Advances in Neural Information Processing Systems 16. MIT Press, Cambridge, MA, USA, 2004.
14. A. Bar-Hillel, T. Hertz, N. Shental, and D. Weinshall. Learning distance functions using equivalence relations. In Proceedings of the Twentieh International Conference on Machine Learning, pages 11-18, 2003.
15. T. Hertz, A. Bar-Hillel, and D. Weinshall. Boosting margin based distance functions for clustering. In Proceedings of the Twenty-First International Conference on Machine Learning, pages 393-400, 2004.
16. M. Schultz and T. Joachims. Learning a distance metric from relative comparisons. In Advances in Neural Information Processing Systems 16. MIT Press, Cambridge, MA, USA, 2004.
17. E.P. Xing, A.Y. Ng, M.I. Jordan, and S. Russell. Distance metric learning, with application to clustering with side-information. In Advances in Neural Information Processing Systems 15, pages 505-512. MIT Press, 2003.
18. H. Chang and D.Y. Yeung. Locally linear metric adaptation for semi-supervised clustering. In Proceedings of the Twenty-First International Conference on Machine Learning, pages 153-160, 2004.
19. W.M. Rand. Objective criteria for the evaluation of clustering methods. Journal of the American Statistical Association, 66:846-850, 1971.