Metric learning for semi-supervised clustering using pairwise constraints and the geometrical structure of data

Intelligent Data Analysis

Volumn 13, Issue 6, 2009, Pages 887-899

  Baghshah, Mahdieh Soleymani ^a   Shouraki, Saeed Bagheri ^a  

^a SHARIF UNIVERSITY OF TECHNOLOGY   (Iran)

Author keywords

Constraints; Laplacian; Metric learning; Semi supervised clustering

Indexed keywords

EID: 77649151199     PISSN: 1088467X     EISSN: 15714128     Source Type: Journal    
DOI: 10.3233/IDA-2009-0399     Document Type: Article

References (30)

1. M.G.H. Omran, A.P. Engelbrecht and A. Salman, An overview of clustering methods, Intelligent Data Analysis 11(6) (2007), 583-605.
2. A.L.N. Fred and A.K. Jain, Combining multiple clusterings using evidence accumulation, IEEE Transactions on Pattern Analysis and Machine Intelligence 27(6) (2005), 835-850.
3. M.H.C. Law, A. Topchy and A.K. Jain, Model-based clustering with probabilistic constraints, in: SIAM Conference on Data Mining, 2005, pp. 641-645.
4. M.H.C. Law, Clustering, dimensionality reduction, and side information, Ph.D. Dissertation, Michigan University, 2006.
5. S. Basu, Semi-supervised clustering: probabilistic models, algorithms and experiments, Ph.D. Dissertation, University of Texas at Austin, 2005.
6. H. Chang and D.Y. Yeung, Locally linear metric adaptation with application to semi-supervised clustering and image retrieval, Pattern Recognition 39 (2006), 1253-1264.
7. S. Basu, A. Banerjee and R.J. Mooney, Semi-supervised clustering by seeding, in: 19th International Conference on Machine Learning (ICML-02), Sydney, Australia, 2002, pp. 19-26.
8. K. Wagstaff, C. Cardie, S. Rogers and S. Schroedl, Constrained K-Means clustering with background knowledge, in: 18th International Conference on Machine Learning (ICML01), 2001, pp. 577-584.
9. Z. Lu and T. Leen, Semi-supervised learning with penalized probabilistic clustering, in: Neural Information Processing Systems 17 (2005), Cambridge, MA, 849-856.
10. L. Zhengdong and T. Leen, Semi-supervised learning with penalized probabilistic clustering, in: Neural Information Processing Systems 17 (2004), 849-856.
11. N. Bansal, A. Blum and S. Chawla, Correlation clustering, Machine Learning 56(1-3) (2004), 89-113.
12. T. Lange, M.H. Law, A.K. Jain and J. Buhmann, Learning with constrained and unlabelled data, in: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, volume 1, 2005, pp. 730-737.
13. Q. Zhao and D.J. Miller, Mixture modeling with pair wise, instance-level class constraints, Neural Computation 17(11) (2005), 2482-2507.
14. S.X. Yu and J. Shi, Segmentation given partial grouping constraints, IEEE Transactions on Pattern Analysis and Machine Intelligence 26(2) (2004), 173-183.
15. B. Kulis, S. Basu, I. Dhillon and R.J. Mooney, Semi-supervised graph clustering: a kernel approach, in: 22nd International Conference on Machine Learning, 2005, pp. 457-464.
16. S. Basu, M. Bilenko and R.J. Mooney, A probabilistic framework for semi-supervised clustering, in: 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2004, pp. 59-68.
17. N. Shental, A. Bar-Hillel, T. Hertz and D. Weinshall, Computing Gaussian mixture models with EM using equivalence constraints, in: Neural Information Processing Systems 16 (2004), Cambridge, MA, MIT Press, 465-472.
18. D. Klein, S.D. Kamvar and C. Manning, From instance-level constraints to space-level constraints: making the most of prior knowledge in data clustering, in: 19th International Conference on Machine Learning (ICML-02), Sydney, Australia, 2002, pp. 307-314.
19. E.P. Xing, A.Y. Ng, M.I. Jordan and S. Russell, Distance metric learning with application to clustering with side information, in: Neural Information Processing Systems 15 (2003), Cambridge, MA, MIT Press, pp. 505-512.
20. M. Bilenko, S. Basu and R.J. Mooney, Integrating constraints and metric learning in semi-supervised clustering, in: 21st International Conference on Machine Learning, 2004, pp. 81-88.
21. 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.
22. T. Hertz, A. Bar-Hillel and D. Weinshall, Boosting margin-based distance functions for clustering, in: Proceedings of 21st International Conference on Machine, 2004.
23. M. Schultz and T. Joachims, Learning a distance metric from relative comparisons, in: Neural Information Processing Systems 16 (2003), Cambridge, MA, MIT Press, 41-48.
24. D.Y. Yeung and H. Chang, Extending the relevant component analysis algorithm for metric learning using both positive and negative equivalence constraints, Pattern Recognition 39 (2006), 1007-1010.
25. D.Y. Yeung and H. Chang, A Kernel Approach for semi-supervised metric learning, IEEE Trans. on Neural Networks 18(1) (2007), 141-149.
26. H. Chang, D.Y. Yeung and W.K. Cheung, Relaxational metric adaptation and its application to semi-supervised clustering and content-based image retrieval, Pattern Recognition 39 (2006), 1905-1917.
27. T. De Bie, M. Momma and N. Cristianini, Efficiently learning the metric with side-information, Lecture Notes in Artificial Intelligence 2842 (2003), 175-189.
28. N. Kumar and K. Kummamuru, Semi-supervised Clustering with Metric Learning using Relative Comparisons, IEEE Trans. on Knowledge and Data Engineering 20(4) (2007), 496-503.
29. M. Belkin and P. Niyogi, Laplacian eigenmaps for dimensionality reduction and data representation, Neural Computation 15(6) (June 2003), 1373-1396.
30. D. Cai, X. He and J. Han, Semi-supervised discriminant analysis, Proc. 2007 IEEE Int. Conf. on Computer Vision (ICCI'07), Brazil, 2007, pp. 1-7.