Kernel relevant component analysis for distance metric learning

Proceedings of the International Joint Conference on Neural Networks

Volumn 2, Issue , 2005, Pages 954-959

  Tsang, Ivor W ^a   Cheung, Pak Ming ^a   Kwok, James T ^a  

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

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; CONTROL NONLINEARITIES; DATA HANDLING; DISTANCE MEASUREMENT; MATHEMATICAL TRANSFORMATIONS; PROBLEM SOLVING; REAL TIME SYSTEMS; VECTOR QUANTIZATION;

CLUSTERING ALGORITHMS; KERNEL METHOD; LINEAR TRANSFORMS; RELEVANT COMPONENT ANALYSIS (RCA);

LEARNING SYSTEMS;

EID: 33745963387     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2005.1555981     Document Type: Conference Paper

References (20)

1. D.W. Aha. Feature weighting for lazy learning algorithms. In H. Liu and H. Motoda, editors. Feature Extraction, Construction and Selection: A Data Mining Perspective. Kluwer, Norwell, MA, 1998.
2. A. Bar-Hillel, T. Hertz, N. Shental, and D. Weinshall. Learning distance functions using equivalence relations. In Proceedings of the Twentieth International Conference on Machine Learning, pages 11-18, Washington, D.C., USA. August 2003.
3. S. Basu, M. Bilenko, and R Mooney. Comparing and unifying search-based and similarity-based approaches to semi-supervised clustering. In ICML 2003 Workshop on The Continuum from Labeled to Unlabeled Data in Machine Learning & Data Mining, Washington, D.C., USA, August 2003.
4. C. Blake, E. Keogh, and C.J. Merz. UCI repository of machine learning databases, 1998. http://www.ics.uci.edu/~mlearn/MLRepository.html University of California, Irvine, Department of Information and Computer Sciences.
5. A.P. Bradley. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition, 30(7): 1145-1159, 1997.
6. S.E. Brenner, P. Koehl, and M. Levitt. The ASTRAL compendium for sequence and structure analysis. Nucleic Acids Research, 28:254-256, 2000.
7. G. Chechik and N. Tishby. Extracting relevant structures with side information. In S. Becker, S. Thrun, and K. Obermayer, editors, Advances in Neural Information Processing Systems 15, Cambridge, MA, 2003. MIT Press.
8. J.T. Kwok and I.W. Tsang. Learning with idealized kernels. In Proceedings of the Twentieth International Conference on Machine Learning, pages 400-407, Washington, D.C., USA, August 2003.
9. C. Leslie, E. Eskin, and W.S. Noble. The spectrum kernel: A string kernel for SVM protein classification. In Proceedings of the Pacific Symposium on Biocomputing, pages 564-575, Lihue, Hawaii, USA, 2002.
10. A.G. Murzin, S.E. Brenner, T. Hubbard, and C. Chothia. SCOP: A structural classification of proteins database for the investigation of sequences and structures. Journal of Molecular Biology, 247:536-540, 1995.
11. B. Schölkopf, A. Smola, and K.R. Müller. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 10:1299-1319, 1998.
12. B. Schölkopf and A.J. Smola. Learning with Kernels. MIT Press, Cambridge, MA, 2002.
13. M. Schultz and T. Joachims. Learning a distance metric from relative comparisons. In S. Thrun, L. Saul, and B. Schölkopf, editors. Advances in Neural Information Processing Systems 16, Cambridge, MA. 2004. MIT Press.
14. N. Shental, T. Hertz, D. Weinshall, and M. Pavel. Adjustment learning and relevant component analysis. In Proceedings of the Seventh European Conference on Computer Vision, volume 4, pages 776-792, Copenhagen, Denmark, 2002.
15. V. Vapnik. Statistical Learning Theory. Wiley, New York, 1998.
16. K. Wagstaff and C. Cardie. Clustering with instance-level constraints. In Proceedings of the Seventeenth International Conference on Machine Learning, pages 1103-1110, Standod, CA. USA, 2000.
17. K. Wagstaff, C. Cardie, S. Rogers, and S. Schroedl. Constrained k-means clustering with background knowledge. In Proceedings of the Eighteenth International Conference on Machine Learning, pages 577-584, Williamstown, MA, USA, 2001.
18. J. Weston, B. Schölkopf, E. Eskin, C. Leslie, and S.W. Noble. Dealing with large diagonals in kernel matrices. Principles of Data Mining and Knowledge Discovery, Springer Lecture Notes in Computer Science 243, 2002.
19. D. Wettschereck, D.W. Aha, and T. Mohri. A review and empirical evaluation of feature weighting methods for a class of lazy learning algorithms. Artificial Intelligence Review, 11:273-314, 1997.
20. E.P. Xing, A.Y. Ng, M.I. Jordan, and S. Russell. Distance metric learning, with application to clustering with side-information. In S. Becker, S. Thrun, and K. Obermayer, editors. Advances in Neural Information Processing Systems 15, Cambridge, MA, 2003. MIT Press.