Noiseless independent factor analysis with mixing constraints in a semi-supervised framework. application to railway device fault diagnosis

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5769 LNCS, Issue PART 2, 2009, Pages 416-425

  Côme, Etienne ^a   Oukhellou, Latifa ^a,b   Denœux, Thierry ^c   Aknin, Patrice ^a  

^a Lab des Technologies Nouvelles   (France)

^b UNIVERSITÉ PARIS EST CRÉTEIL   (France)

^c CNRS   (France)

Author keywords

Diagnosis; Independent Factor Analysis; Mixing constraints; Railway device; Semi supervised learning

Indexed keywords

CLUSTER MEMBERSHIPS; FAULT DIAGNOSIS; INDEPENDENT FACTOR ANALYSIS; MIXING PROCESS; MIXTURES OF GAUSSIANS; OBSERVED DATA; PARTIAL KNOWLEDGE; PARTIALLY SUPERVISED LEARNING; PRIOR INFORMATION; RAILWAY DEVICE; REAL-WORLD APPLICATION; SEMI-SUPERVISED; SEMI-SUPERVISED LEARNING; SOURCE DENSITY;

BACKPROPAGATION; FAILURE ANALYSIS; INDUCTIVELY COUPLED PLASMA; LEARNING SYSTEMS; MIXING; RAILROADS;

NEURAL NETWORKS;

EID: 70450158530     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-04277-5_42     Document Type: Conference Paper

References (19)

1. Hyvärinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. Wiley, Chichester (2001)
2. Bell, A.J., Sejnowski, T.J.: An information maximization approach to blind separation and blind deconvolution. Neural Computation 7(6), 1129-1159 (1995)
3. Jutten, C., Comon, P.: Séparation de source 2, au-delà de l'aveugle et application. Hermés (2007)
4. Moulines, E., Cardoso, J., Cassiat, E.: Maximum likelihood for blind separation and deconvolution of noisy signals using mixture models. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 3617-3620 (1997)
5. Attias, H.: Independent factor analysis. Neural Computation 11(4), 803-851 (1999)
6. Attias, H.: Independent factor analysis with temporally structured factors. In: Proceedings of the 12th NIPS Conference, pp. 386-392. MIT Press, Cambridge (2000)
7. Moussaoui, S., Hauksdóttir, H., Schmidt, F., Jutten, C., Chanussot, J., Brie, D., Douté, D., Benediktosson, J.: On the decomposition of Mars Hyperspectral data by ICA and Bayesian positive source separation. Neurocomputing for Vision Research; Advances in Blind Signal Processing 71, 2194-2208 (2008)
8. Hyvärinen, A., Karthikesh, R.: Imposing sparsity on the mixing matrix in independent component analysis. Neurocomputing 49(1), 151-162 (2002)
9. Zhang, K., Chan, L.W.: ICA with sparse connections. In: Proceedings of Intelligent Data Engineering and Automated Learning Conference (IDEAL), pp. 530-537. Springer, Heidelberg (2006)
10. Bartholomew, D.J., Martin, K.: Latent variable models and factor analysis, 2nd edn. Kendall's library of satistics, Arnold (1999)
11. Bakir, T., Peter, A., Riley, R., Hackett, J.: Non-negative maximum likelihood ICA for blind source separation of images and signals with application to hyperspectral image subpixel demixing. In: Proceedings of the IEEE International Conference on Image Processing, pp. 3237-3240 (2006)
12. Bollen, K.A.: Structural Equations with Latent Variables. Wiley, Chichester (1989)
13. Cǒme, E.: Apprentissage de modéles génératifs pour le diagnostic de systèmes complexes avec labellisation douce et contraintes spatiales PhD thesis, Université de Technologie de Compiégne (2009)
14. Amari, S., Cichocki, A., Yang, H.H.: A New Learning Algorithm for Blind Signal Separation. In: Proceedings of the 8th Conference on Advances in Neural Information Processing Systems (NIPS). MIT Press, Cambridge (1995)
15. Shafer, G.: A mathematical theory of evidence. Princeton University Press, Princeton (1976)
16. Cǒme, E., Oukhellou, L., Denœux, T., Aknin, P.: Learning from partially supervised data using mixture models and belief functions. Pattern recognition 42, 334-348 (2009)
17. Debiolles, A., Oukhellou, L., Denoeux, T., Aknin, P.: Output coding of spatially dependent subclassifiers in evidential framework. Application to the diagnosis of railway track-vehicle transmission system. In: Proceedings of FUSION 2006, Florence, Italy (July 2006)
18. Mclachlan, G.J., Krishnan, T.: The EM algorithm and Extension. Wiley, Chichester (1996)
19. Cichocki, A., Amari, S.: Adaptive Blind Signal and Image Processing. Wiley, Chichester (2002)