GSML: A unified framework for sparse metric learning

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2009, Pages 189-198

  Huang, Kaizhu ^a   Ying, Yiming ^b   Campbell, Colin ^b  

^a INSTITUTE OF AUTOMATION   (China)

^b UNIVERSITY OF BRISTOL   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

BENCHMARK DATASETS; DIMENSION REDUCTION; DISTANCE METRICS; ITERATIVE OPTIMIZATION; LARGE DATASETS; LOW-DIMENSIONAL REPRESENTATION; METRIC LEARNING; NEAREST NEIGHBORS; REAL-WORLD; RELEVANT VECTOR MACHINES; STATE-OF-THE-ART METHODS; TEST ACCURACY; UNIFIED FRAMEWORK; VECTOR MACHINES;

LEARNING ALGORITHMS;

EID: 77951154868     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2009.22     Document Type: Conference Paper

References (23)

1. S. Agarwal, J. Wills, L. Gayton, G. Lanckriet, D. Kriegman, and S. Belongie. Generalized nonmetric multidimensional scaling. In International Conference on Artificial Intelligence and Statistics (AISTAT'08), 2008.
2. A. Argyriou, T. Evgeniou, and M. Pontil. Multi-task feature learning. In Advances in Neural Information Processing Systems (NIPS) 18, 2006.
3. V. Athitsos, J. Alton, S. Sclaroff, and G. Kollios. Boostmap: A method for efficient approximate similarity rankings. In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2004.
4. A. Bar-Hillel, T. Hertz, N. Shental, and D. Weinshall. Learning a mahalanobis metric from equivalence constraints. Journal of Machine Learning Research, 6:937-965, 2005.
5. S. Chopra, R. Hadsell, and Y. LeCun. Learning a similarity metric discriminatively, with application to face verification. In Proceedings of 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR-2005), 2005.
6. T. Cox and M. Cox. Multidimensional Scaling. Chapman & Hall, London, 1994.
7. J. Davis, B. Kulis, P. Jain, S. Sra, and I. Dhillon. Information- theoretic metric learning. In Intermational Conference on Machine Learning (ICML), 2007.
8. G. Fung, O. L. Mangasarian, and A. J. Smola. Minimal kernel classifers. Journal of Machine Learning Research, 3:303-321, 2002.
9. G. Fung, R. Rosales, and R. B. Rao. Feature selection and kernel design via linear programming. In Proceedings of Internet Joint Conference on Artificial Intelligence (IJCAI), pages 786-791, 2007.
10. J. Goldberger, S. Roweis, G. Hinton, and R. Salakhutdinov. Neighbourhood component analysis. In Advances in Neural Information Processing Systems (NIPS), 2004.
11. T. Hastie, R. Tibshirani, and R. Friedman. The Elements of Statistical Learning. Springer-Verlag New York, LLC, 2003.
12. I. T. Jolliffe. Principal Component Analysis. Springer- Verlag, New York, 1989.
13. C. A. Micchelli and M. Pontil. Learning the kernel function via regularization. Journal of Machine Learning Research, 6:1099-1125, 2005.
14. H. Ouyang and A. Gray. Learning dissimilarities by ranking: from sdp to qp. In Proceedings of the Twentyfive International Conference on Machine learning (ICML-2008), 2008.
15. R. Rosales and G. Fung. Learning sparse metrics via linear programming. In KDD, pages 367-373, 2006.
16. S. T. Roweis and L. K. Saul. Nonlinear dimensionality reduction by locally linear embedding. Science, (290):123-137, 2000.
17. L. Song, A. Smola, K. Borgwardt, and A. Gretton. Colored maximum variance unfolding. In Advances in Neural Information Processing Systems (NIPS), 2008.
18. M. Sugiyama. Dimensionality reduction of multimodal labeled data by local fisher discriminant analysis. Journal of Machine Learning Research, 18:1027- 1061, 2007.
19. L. Torresani and K. Lee. Large margin component analysis. In Advances in Neural Information Processing Systems (NIPS), 2007.
20. Jean-Philippe Vert, Jian Qiu, and William S. Nobel. A new pairwise kernel for biological network inference with support vector machines. BMC Bioinformatics, 8, 2007.
21. K.Weinberger, J. Blitzer, and L. Saul. Distance metric learning for large margin nearest neighbor classification. In Advances in Neural Information Processing Systems (NIPS), 2006.
22. K. Weinberger and L. Saul. Fast solvers and efficient implentations for distance metric learning. In Proceedings of the twenty-fifth international conference on Machine learning (ICML-2008), 2008.
23. 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 Systems (NIPS), 2002.