A semi-supervised learning algorithm for growing neural gas in face recognition

Journal of Mathematical Modelling and Algorithms

Volumn 7, Issue 4, 2008, Pages 425-435

  Zaki, Shireen M ^a   Yin, Hujun ^a  

^a UNIVERSITY OF MANCHESTER   (United Kingdom)

Author keywords

Classifier; Face recognition; Growing neural gas; Partly labelled data; Semi supervised learning

Indexed keywords

EID: 57949086833     PISSN: 15701166     EISSN: 15729214     Source Type: Journal    
DOI: 10.1007/s10852-008-9095-8     Document Type: Article

References (22)

1. Balcan, M.-F., Blum, A., Choi, P., Lafferty, J., Pantano, B., Rwebangira, M.R., Zhu, X.: Person identification in webcam images: an application of semi-supervised learning. In: ICML 2005 Workshop on Learning with Partially Classified Training Data, Bonn, August 2005
2. Baluja, S.: Probabilistic modeling for face orientation discrimination: learning from labeled and unlabeled examples. In: Kearns, M.S., Solla, S.A., Cohn, D.A. (eds.) Advances in Neural Information Processing Systems 11. MIT, Cambridge (1999)
3. Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifier. In: Proc. 5th ACM Workshop on Computational Learning Theory, pp. 144-152, Pittsburgh, 27-29 July 1992
4. A. Bouchachia 2006 Learning with partly labeled data Neural Comput. Appl. 16 267 293
5. Castelli, V.: The relative value of labeled and unlabeled samples in pattern recognition. Ph.D. Dissertation, Stanford University (1994)
6. Chapelle, O., Schölkopf, B., Zien, A. (eds.) Semi-Supervised Learning. MIT, Boston (2006)
7. Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines. http://www.csie.ntu.edu.tw/~cjlin/libsvm (2001)
8. N.V. Chawla G. Karakoulas 2005 Learning from labeled and unlabeled data: an empirical study across techniques and domains J. Artif. Intell. Res. 25 331 366
9. Cozman, F.G., Cohen, I.: The effect of modeling errors in semi-supervised learning of mixture models: how unlabeled data can degrade performance of generative classifiers. Technical Report, Univ. of Sao Paulo (2003)
10. F.G. Cozman I. Cohen M.C. Cirelo 2003 Semi-supervised learning of mixture models Proc. Int. Conf. Mach. Learning 20 99 106
11. Dara, R., Kremer, S.C., Stacey, D.A.: Clustering unlabelled data with SOMs improves classification of labelled real-world data. In: Proc. IEEE World Congress on Computational Intelligence, pp. 2237-2242, Honolulu, 12-17 May 2002
12. A.P. Dempster N.M. Laird D.B. Rubin 1977 Maximum likelihood from incomplete data via the EM algorithm J. R. Stat. Soc. B 39 1 38
13. B. Fritzke 1994 Growing cell structures-a self-organising network for unsupervised and supervised Learning Neural Netw. 7 144 1460
14. Fritzke, B.: A growing neural gas network learns topology. In: Advances in Neural Information Processing Systems 7. MIT, Cambridge (1995)
15. Kohonen, T.: Self-Organizing Maps. Springer, Berlin (1990)
16. Martinetz, T.: Competitive Hebbian learning rule forms perfectly topology preserving maps. In: Proc. Int. Conf. on Artificial Neural Networks (ICANN'93), pp. 426-438 (1993)
17. T.M. Martinetz S.G. Berkovich K.J. Schulten 1993 Neural gas-network for vector quantization and its application to time-series prediction IEEE Trans. Neural Netw. 4 558 569
18. Nigam, K., McCallum, A., Mitchell, T.: Semi-supervised text classification using EM. In: Chapelle, O., Schölkopf, B., Zien, A. (eds.) Semi-Supervised Learning. MIT, Boston (2006)
19. Samaria, F., Harter, A.: Parameterisation of a stochastic model for human face identification (ORL face databse). In: 2nd IEEE Workshop on Applications of Computer Vision. Olivetti Research Laboratory (1994)
20. Seeger, M.: Learning with labelled and unlabelled data. Technical Report, Edinburgh University (2001)
21. Turk, M., Pentland, A.: Face recognition using eigenfaces. Proc. IEEE Conf. Comp. Vis. Patt. Recogn. 586-591 (1991)
22. Zhu, X.: Semi-supervised learning literature survey. Technical Report, University of Wisconsin - Madison (2005)