Kernel-based metric learning for semi-supervised clustering

Neurocomputing

Volumn 73, Issue 7-9, 2010, Pages 1352-1361

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

^a SHARIF UNIVERSITY OF TECHNOLOGY   (Iran)

Author keywords

Kernel learning; Metric learning; Non parametric kernel matrix; Optimization; Pairwise constraint; Semi supervised clustering

Indexed keywords

KERNEL LEARNING; KERNEL MATRICES; METRIC LEARNING; NON-PARAMETRIC; PAIRWISE CONSTRAINTS; SEMI-SUPERVISED CLUSTERING;

CONVEX OPTIMIZATION; LINEAR TRANSFORMATIONS; MATRIX ALGEBRA; STRUCTURAL OPTIMIZATION;

LEARNING ALGORITHMS;

ANALYTIC METHOD; ARTICLE; CLUSTER ANALYSIS; INTERMETHOD COMPARISON; KERNEL METHOD; MATHEMATICAL ANALYSIS; PRIORITY JOURNAL; PROCESS OPTIMIZATION; SEMI SUPERVISED CLUSTERING;

EID: 77949267074     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2009.12.009     Document Type: Article

References (30)

1. Bar-Hillel A., Hertz T., Shental N., and Weinshall D. Learning a Mahalanobis metric from equivalence constraints. Journal of Machine Learning Research 6 (2005) 937-965
2. Belkin M., and Niyogi P. Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation 15 6 (2003) 1373-1396
3. Boyd S., and Vandenberghe L. Convex Optimization (2004), Cambridge University Press, Cambridge
4. Chang H., and Yeung D.-Y. Kernel-based distance metric learning for content-based image retrieval. Image and Vision Computing 25 5 (2007) 695-703
5. Chang H., and Yeung D.Y. Locally linear metric adaptation with application to semi-supervised clustering and image retrieval. Pattern Recognition 39 7 (2006) 1253-1264
6. Domeniconi C., Peng J., and Gunopulos D. Locally adaptive metric nearest neighbor classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 24 9 (2002) 1281-1285
7. Domeniconi C., Gunopulos D., and Peng J. Large margin nearest neighbor classifiers. IEEE Transactions on Neural Networks 16 4 (2005) 899-909
8. J.H. Friedman, Flexible metric nearest neighbor classification, Technical Report, Statistics Department, Stanford University, 1994.
9. Fukunaga K., and Hostetler L. Optimization of k-nearest neighbor density estimation. IEEE Transactions on Information Theory 19 3 (1973) 320-326
10. Fukunaga K., and Flick T.E. An optimal global nearest neighbor metric. IEEE Transactions on Pattern Analysis and Machine Intelligence 6 3 (1984) 314-318
11. Hastie T., and Tibshirani R. Discriminant adaptive nearest neighbor classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 18 6 (1996) 607-616
12. S.C.H. Hoi, W. Liu, M.R. Lyu, W.-Y. Ma, Learning distance metrics with contextual constraints for image retrieval, in: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06), Oregon State University, Corvallis, OR, USA, 2006, pp. 2072-2078.
13. S.C.H. Hoi, R. Jin, M.R. Lyu, Learning nonparametric kernel matrices from pairwise constraints, in: 24th International Conference on Machine Learning (ICML'07), New York, USA, 2007, pp. 361-368.
14. Kumar N., and Kummamuru K. Semi-supervised clustering with metric learning using relative comparisons. IEEE Transactions on Knowledge and Data Engineering 20 4 (2007) 496-503
15. Lanckriet G.R.G., Cristianini N., Bartlett P., Ghaoui L.E., and Jordan M.I. Learning the kernel matrix with semi-definite programming. Journal of Machine Leaning Research 5 1 (2004) 27-72
16. M.H.C. Law, Clustering, dimensionality reduction, and side information, Ph.D. Dissertation, Michigan University, 2006.
17. J. Löfberg, YALMIP: A toolbox for modeling and optimization in MATLAB, in: IEEE International Symposium on Computer Aided Control Systems Design (CACSD'04), 2004, pp. 284-289.
18. Lowe D.G. Similarity metric learning for a variable-kernel classifier. Neural Computation 7 1 (1995) 72-85
19. Rand W.M. Objective criteria for the evaluation of clustering methods. Journal of the American Statistical Association 66 (1971) 846-850
20. M. Schultz, T. Joachims, Learning a distance metric from relative comparisons, in: Advances in NIPS 16, MIT Press, Cambridge, MA, USA, 2004, pp. 41-48.
21. Short R.D., and Fukunaga K. The optimal distance measure for nearest neighbor classification. IEEE Transactions on Information Theory 27 5 (1981) 622-627
22. M. Soleymani Baghshah, S. Bagheri Shouraki, Semi-supervised metric learning using pairwise constraints, in: 21st International Joint Conference on Artificial Intelligence (IJCAI'09), 2009, pp. 1217-1222.
23. J.F. Strum and the Advanced Optimization Laboratory at McMaster University, Canada, SeDuMi version 1.1R3, 2006. Available at: 〈http://sedumi.mcmaster.ca/〉.
24. Wang D., Lim J.S., Han M.-M., and Lee B.-W. Learning similarity for semantic images classification. Neurocomputing 67 (2005) 363-368
25. Xiang S., Nie F., and Zhang C. Learning a Mahalanobis distance metric for data clustering and classification. Pattern Recognition 41 12 (2008) 3600-3612
26. E.P. Xing, A.Y. Ng, M.I. Jordan, S. Russell, Distance metric learning with application to clustering with side information, in: Advances in NIPS 15, MIT Press, Cambridge, MA, USA, 2003, pp. 505-512.
27. L. Yang, R. Jin, Distance metric learning: a comprehensive survey, Technical Report, Michigan State University, 2006.
28. Yeung D.Y., and Chang H. Extending the relevant component analysis algorithm for metric learning using both positive and negative equivalence constraints. Pattern Recognition 39 5 (2006) 1007-1010
29. Yeung D.Y., and Chang H. A Kernel Approach for semi-supervised metric learning. IEEE Transactions on Neural Networks 18 1 (2007) 141-149
30. Yeung D.Y., Chang H., and Dai G. A scalable kernel-based semi-supervised metric learning algorithm with out-of-sample generation ability. Neural Computation 20 11 (2008) 2839-2861