Semi-supervised IFA with prior knowledge on the mixing process an application to a railway device diagnosis

Proceedings - 7th International Conference on Machine Learning and Applications, ICMLA 2008

Volumn , Issue , 2008, Pages 415-420

  Côme, Etienne ^a   Cherfi, Zohra Leila ^a   Oukhellou, Latifa ^a,b   Aknin, Patrice ^a  

^a Lab des Technologies Nouvelles   (France)

^b UNIVERSITÉ PARIS EST CRÉTEIL   (France)

Author keywords

[No Author keywords available]

Indexed keywords

APPLICATIONS; INDUCTIVELY COUPLED PLASMA; RAILROADS; ROBOT LEARNING;

DEVICE DIAGNOSIS; GAUSSIANS; INCORPORATING PRIOR KNOWLEDGE; INDEPENDENT COMPONENTS; INDEPENDENT FACTOR ANALYSIS; LEARNING CONTEXTS; MIXING PROCESS; MIXTURE OF GAUSSIANS; PARTIAL KNOWLEDGE; PARTIALLY SUPERVISED LEARNING; PRIOR KNOWLEDGE; SEMI-SUPERVISED;

EDUCATION;

EID: 60649091904     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICMLA.2008.72     Document Type: Conference Paper

References (13)

1. O. Chapelle, B. Schölkopf, A. Zien (Eds.), Semi-Supervised Learning, MIT Press, Cambridge, Ma, 2006.
2. D. Zhang, Z.H. Zhou, and S. Chen, Semi-supervised dimensionality reduction, in: Proceedings of the 7th SIAM Int. Conf. on Data Mining, Minneapolis, 2007.
3. X. Zhu, Semi-supervised learning literature survey, Tech. Report 1530, University of Wisconsin at Madison, WI, 2006. http://www.cs.wisc.edu/~jerryzhu/ pub/ssl-survey.
4. th Conf. of IFCS-2000, Springer, Belgium, 2000, pp. 161-166.
5. T. Denoeux, L.M. Zouhal, Handling possibilistic labels in pattern classification using evidential reasoning, Fuzzy Sets and Systems 122 (3) (2001) 47-62.
6. A. Hyvarinen, Independent Component Analysis, Inter-Science, Wiley, 2001.
7. A. J. Bell and T.J. Sejnowski, An information maximization approach to blind separation and blind deconvolution, Neural Computation, 7 (6): 1129-1159, 1995.
8. S. Amari, A. Cichocki, and H. H. Yang. A new learning algorithm for blind signal separation. In Advances in Neural Information Processing Systems, volume 8, pages 757-763. The MIT Press, 1996.
9. H. Attias, Independent factor analysis, Neural Computation, 11 (4):803-851, 1999.
10. E. Moulines, J. Cardoso, and E. Cassiat, Maximum likelihood for blind separation and deconvolution of noisy signals using mixture models, In ICASSP, volume 5, pages 3617-3620, 1997.
11. A. P. Dempster, N. M. Laird and D. B. Rubin, Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society, B 39:1-38, 1977.
12. A. P. Dempster, Upper and lower probabilities induced by a multivalued mapping, Annals of Mathematical statistics, 38:325-339, 1967.
13. E. Côme, L. Oukhellou, T. Denoeux, and P. Aknin, learning from partially supervised data using mixture models and belief functions, Pattern recognition, accepted for publication, 2008.