Incorporating privileged information through metric learning

IEEE Transactions on Neural Networks and Learning Systems

Volumn 24, Issue 7, 2013, Pages 1086-1098

  Fouad, Shereen ^a   Tino, Peter ^a   Raychaudhury, Somak ^a   Schneider, Petra ^a  

^a UNIVERSITY OF BIRMINGHAM   (United Kingdom)

Author keywords

Distance metric learning (DML); generalized matrix learning vector quantization (GMLVQ); information theoretic metric learning (ITML); learning using privileged information (LUPI)

Indexed keywords

CLASSIFICATION ACCURACY; CLASSIFICATION PROCESS; COMPETITIVE PERFORMANCE; DISTANCE METRIC LEARNING; GENERALIZED MATRIX LEARNING VECTOR QUANTIZATION (GMLVQ); INFORMATION THEORETIC METRIC LEARNING (ITML); LEARNING USING PRIVILEGED INFORMATION (LUPI); LEARNING VECTOR QUANTIZATION;

INFORMATION THEORY; INFORMATION USE; VECTOR QUANTIZATION;

CLASSIFICATION (OF INFORMATION);

ALGORITHM; ARTIFICIAL INTELLIGENCE; BIOMETRY; FACTUAL DATABASE; HUMAN; INFORMATION SCIENCE; LEARNING;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BIOMETRY; DATABASES, FACTUAL; HUMANS; INFORMATION THEORY; LEARNING;

EID: 84877924976     PISSN: 2162237X     EISSN: 21622388     Source Type: Journal    
DOI: 10.1109/TNNLS.2013.2251470     Document Type: Article

References (41)

1. V. Vapnik and A. Vashist, "A new learning paradigm: Learning using privileged information," Neural Netw. , vol. 22, nos. 5-6, pp. 544-557, 2009.
2. V. Vapnik and S. Kotz, Estimation of Dependences Based on Empirical Data: Empirical Inference Science (Information Science and Statistics). New York, USA: Springer-Verlag, 2006.
3. J. Cervantes, X. Li, and W. Yu, "Multi-class SVM for large data sets considering models of classes distribution," in Proc. Int. Conf. Data Mining, 2008, pp. 30-35.
4. P. Schneider, M. Biehl, and B. Hammer, "Adaptive relevance matrices in learning vector quantization," Neural Comput. , vol. 21, no. 12, pp. 3532-3561, 2009.
5. P. Schneider, "Advanced methods for prototype-based classification," Ph. D. dissertation, Dept. Math. Natural Sci. , Univ. Groningen, Groningen, The Netherlands, 2010.
6. J. Cervantes, X. Li, W. Yu, and K. Li, "Support vector machine classification for large data sets via minimum enclosing ball clustering," in Proc. 4th Int. Symp. Neural Netw. , Algorithms Appl. , vol. 71, 2008, pp. 611-619.
7. J. Feyereisl and U. Aickelin, "Privileged information for data clustering," Inf. Sci. , vol. 194, pp. 4-23, Jul. 2012.
8. C. Cortes and V. Vapnik, "Support-vector networks," Mach. Learn. , vol. 20, no. 3, pp. 273-297, Sep. 1995.
9. T. Kohonen, The Handbook of Brain Theory and Neural Networks, 2nd ed. Cambridge, MA, USA: MIT Press, 2003.
10. T. Kohonen, "Learning vector quantization for pattern recognition," Dept. Tech. Phys. , Helsinki Univ. Technology, Espoo, Finland, Tech. Rep. TKK-F-A601, 1986.
11. A. Sato and K. Yamada, "Generalized learning vector quantization," Advances in Neural Information Processing Systems, vol. 7. Cambridge, MA, USA: MIT Press, 1995, pp. 423-429.
12. B. Hammer and T. Villmann, "Generalized relevance learning vector quantization," Neural Netw. , vol. 15, nos. 8-9, pp. 1059-1068, Oct. 2002.
13. B. Hammer, M. Strickert, and T. Villmann, "Relevance LVQ versus SVM," in Artificial Intelligence and Soft Computing (Lecture Notes in Artificial Intelligence), vol. 3070. New York, USA: Springer-Verlag, 2004, pp. 592-597. (Pubitemid 38835885)
14. J. Goldberger, S. Roweis, G. Hinton, and R. Salakhutdinov, "Neighbourhood components analysis," in Advances in Neural Information Processing Systems, vol. 17. Cambridge, MA, USA: MIT Press, 2004, pp. 513-520.
15. K. Q. Weinberger and L. K. Saul, "Distance metric learning for large margin nearest neighbor classification," J. Mach. Learn. Res. , vol. 10, pp. 207-244, Jun. 2009.
16. J. V. Davis, B. Kulis, P. Jain, S. Sra, and I. S. Dhillon, "Informationtheoretic metric learning," in Proc. 24th Int. Conf. Mach. Learn. , 2007, pp. 209-216.
17. A. Bar-Hillel, T. Hertz, N. Shental, and D. Weinshall, "Learning distance functions using equivalence relations," in Proc. 20th Int. Conf. Mach. Learn. , 2003, pp. 11-18.
18. S. C. Hoi, W. Liu, M. R. Lyu, and W. Ma, "Learning distance metrics with contextual constraints for image retrieval," in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. , vol. 2. 2006, pp. 2072-2078. (Pubitemid 44931570)
19. E. P. Xing, A. Y. Ng, M. I. Jordan, and S. J. Russell, "Distance metric learning with application to clustering with side-information," in Neural Information Processing Systems, vol. 15. Cambridge, MA, USA: MIT Press, 2002, pp. 505-512.
20. A. Bar-Hillel, T. Hertz, N. Shental, and D. Weinshall, "Learning a Mahalanobis metric from equivalence constraints," J. Mach. Learn. Res. , vol. 6, pp. 937-965, Dec. 2005.
21. L. Yang and A. R. Jin, "Distance metric learning: A comprehensive survey," Dept. Comput. Sci. Eng. , Univ. Michigan, Ann Arbor, MI, USA, Tech. Rep. , 2006.
22. R. Jin, S. Wang, and Y. Zhou, "Regularized distance metric learning: Theory and algorithm," in Advances in Neural Information Processing. Fort Atkinson, WI, USA: Curran Associates Inc. , 2009, pp. 862-870.
23. W. Bian and D. Tao, "Learning a distance metric by empirical loss minimization," in Proc. 22nd Int. Joint Conf. Artif. Intell. , vol. 2. 2011, pp. 1186-1191.
24. W. Bian and D. Tao, "Constrained empirical risk minimization framework for distance metric learning," IEEE Trans. Neural Netw. Learn. Syst. , vol. 23, no. 8, pp. 1194-1205, Aug. 2012.
25. G. J. McLachlan, Discriminant Analysis and Statistical Pattern Recognition (Wiley series in probability and mathematical statistics). New York, USA: Wiley, 1992.
26. D. Tao, X. Li, X. Wu, and S. J. Maybank, "Geometric mean for subspace selection," IEEE Trans. Pattern Anal. Mach. Intell. , vol. 31, no. 2, pp. 260-274, Feb. 2009.
27. W. Bian and D. Tao, "Max-min distance analysis by using sequential SDP relaxation for dimension reduction," IEEE Trans. Pattern Anal. Mach. Intell. , vol. 33, no. 5, pp. 1037-1050, May 2011.
28. N. J. Higham, "Matrix nearness problems and applications," in Applications of Matrix Theory, M. J. C. Gover and S. Barnett, Eds. Manchester, England: Manchester Univ. Press, 1989, pp. 1-27.
29. B. Kulis, P. Jain, and K. Grauman, "Fast similarity search for learned metrics," IEEE Trans. Pattern Anal. Mach. Intell. , vol. 31, no. 12, pp. 2143-2157, Dec. 2009.
30. C. Darken, J. Chang, and J. Moody, "Learning rate schedules for faster stochastic gradient search," in Proc. IEEE Workshop Neural Netw. Signal Process. , Aug. -Sep. 1992, pp. 3-12.
31. A. Frank and A. Asuncion, "UCI machine learning repository," Dept. Comput. Sci. , Univ. Sydney, N. S. W. , Australia, Tech. Rep. , 2010.
32. C. Elting, C. A. L. Bailer-Jones, and K. W. Smith, "Photometric classification of stars, galaxies and quasars in the sloan digital sky survey DR6 using support vector machines," in Proc. Int. Conf. , Classification Discovery Large Astron. Surveys, vol. 1082. 2008, pp. 9-14.
33. N. M. Ball and R. J. Brunner, "Data mining and machine learning in astronomy," Instrum. Methods for Astrophys. Int. J. Modern Phys. , vol. 91, pp. 1049-1106, Mar. 2010.
34. D. B. Wijesinghe, A. M. Hopkins, B. C. Kelly, N. Welikala, and A. J. Connolly, "Morphological classification of galaxies and its relation to physical properties," Monthly Notices Royal Astron. Soc. , vol. 404, no. 4, pp. 2077-2086, 2010.
35. J. de la Calleja and O. Fuentes, "Machine learning and image analysis for morphological galaxy classification," Monthly Notices Royal Astron. Soc. , vol. 349, no. 1, pp. 87-93, 2004. (Pubitemid 38422618)
36. S. Kasivajhula, N. Raghavan, and H. Shah, "Morphological galaxy classification using machine learning," Monthly Notices Royal Astron. Soc. , vol. 8, pp. 1-8, May 2007.
37. C. Reichardt, R. Jimenez, and A. Heavens, "Recovering physical parameters from galaxy spectra using moped," Monthly Notices Royal Astron. Soc. , vol. 327, no. 3, pp. 849-867, 2001. (Pubitemid 33629320)
38. C. J. Lintott, K. Schawinski, A. Slosar, K. Land, S. Bamford, D. Thomas, M. J. Raddick, R. C. Nichol, A. Szalay, D. Andreescu, P. Murray, and J. Berg, "Galaxy Zoo: Morphologies derived from visual inspection of galaxies from the sloan digital sky survey," Monthly Notices Royal Astron. Soc. , vol. 389, no. 3, pp. 1179-1189, Apr. 2008.
39. C. J. Lintott, K. Schawinski, S. Bamford, A. Slosar, K. Land, D. Thomas, E. Edmondson, K. Masters, R. Nichol, J. Raddick, A. Szalay, D. Andreescu, P. Murray, and J. Vandenberg, "Galaxy Zoo 1: Data release of morphological classifications for nearly 900 000 galaxies," Monthly Notices Royal Astron. Soc. , vol. 410, no. 1, pp. 166-178, 2011.
40. K. Abazajian, "The seventh data release of the sloan digital sky survey," Astrophys. J. Suppl. , vol. 82, no. 2, pp. 543-558, 2009.
41. M. Banerji, O. Lahav, C. J. Lintott, F. B. Abdalla, K. Schawinski, S. P. Bamford, D. Andreescu, P. Murray, M. J. Raddick, A. Slosar, A. Szalay, D. Thomas, and J. Vandenberg, "Galaxy Zoo: Reproducing galaxy morphologies via machine learning," Monthly Notices Royal Astron. Soc. , vol. 406, no. 1, pp. 342-353, 2010.