BeSOM: Bernoulli on self-organizing map

IEEE International Conference on Neural Networks - Conference Proceedings

Volumn , Issue , 2007, Pages 631-636

  Lebbah, Mustapha ^a   Rogovschi, Nicoleta ^a   Bennani, Younès ^a  

^a UNIVERSITÉ PARIS 13   (France)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; BOOLEAN FUNCTIONS; COMPUTER NETWORKS; CONFORMAL MAPPING; FLOW OF SOLIDS; LEARNING ALGORITHMS; LEARNING SYSTEMS; NEURAL NETWORKS; PROBABILITY; PROBABILITY DISTRIBUTIONS; PROGRAMMING THEORY; RISK ASSESSMENT;

BINARY DATA; DATA SETS;

CLUSTER ANALYSIS;

EID: 51749089534     PISSN: 10987576     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2007.4371030     Document Type: Conference Paper

References (24)

1. F. Anouar, F. Badran, and S. Thiria. Self-organizing map, a probabilistic approach. In Proceedings of WSOM'97, Workshop on Self-Organizing Maps, Espoo, Finland, June 4-6, pages 339-344. Helsinki University of Technology, Neural Networks Research Centre, Espoo, Finland, 1997.
2. M. Lebbah, S. Thiria, F. Badran, Topological Map for Binary Data, Topological Map for Binary Data, ESANN 2000, Bruges, April 26-27-28, pp. 267-272. 2000. Proceedings.
3. J.J. Verbeek, N. Vlassis, B.J.A. Krse: Self-organizing mixture models. Neurocomputing 63, pp. 99-123. 2005.
4. C. M. Bishop, M. Svensén, and C. K. I.Williams, "GTM: The generative topographic mapping," Neural Comput., vol. 10, no. 1, p 215-234. 1998.
5. G. Celeux, F. Forbes, N. Payrard, EM procedures using mean field-like approximations for Markov model-based image segmentation, Pattern Recognition 36, pp. 131-144. 2003.
6. G.Govaert. Classification binaire et modles. Revue de Statistique Applique, 38 no. 1 p. 67-81.1990.
7. M. Nadif and G. Govaert. Clustering for binary data and mixture models : Choice of the model. Applied Stochastic Models and Data Analysis, 13 :pp.269-278.1998.
8. A.P. Dempster, N.M. Laird, D.B. Rubin, Maximum likelihood from incomplete data via the EM algorithm, J. Roy. Statist. Soc. 39 (1), pp.1-38. 1977.
9. McLachlan, G. Krishman, T. The EM algorithm and Extensions. Wiley, New York. 1997.
10. A. Kaban, M. Girolami, A combined latent class and trait model for the analysis and visualization of discrete data, IEEE Trans. Pattern Anal. Mach. Intell. 23, pp. 859-872. 2001.
11. M. Girolami, The Topographic Organisation and Visualisation of Binary Data using Mutivariate-Bernoulli Latent Variable Models. I.E.E.E Transactions on Neural Networks. 12(6). pp 1367-1374. 2001.
12. M. E. Tipping, Probabilistic visualization of high-dimensional binary data, Advances Neural Inform. Processing Syst., pp. 592-598. 1998.
13. S. Ibbou, M. Cottrell, Multiple correspondance Analysis crosstabulation matrix using the Kohonen algorithm. In verlaeysen, M. Editor proc of ESANN'95, pages 27-32. Dfacto Bruxelles.1995.
14. S. Kaski, T. Honkela, K. Lagus, and T. Kohonen. WEBSOM-self-organizing maps of document collections. Neurocomputing, volume 21, pages 101-117. 1998.
15. T. Kostiainen, J. Lampinen, On the generative probability density model in the self-organizing map, Neurocomputing 48, pp. 217-228. 2002.
16. T. Kohonen, Self-organizing Maps, Springer, Berlin, 2001.
17. A. Kaban, E. Bingham, and T. Hirsimki. Learning to read between the lines: The aspect Bernoulli model. SIAM Intl. Conf. Data Mining. 2004.
18. A. Patrikainen and H. Mannila. Subspace clustering of high-dimensional binary data - A probabilistic approach. Proc. Workshop on Clustering High Dimensional Data in SIAM International Conference on Data Mining. 2004.
19. F. Leich, A. Weingessel, E. Dimitriadou. Competitive Learning for Binary Data. Proc of ICANN'98, septembre 2-4. Springer Verlag. 1998.
20. S. P Luttrel . A Bayesian Ananlysis of Self-Organizing Maps, Neural Computing vol 6, pp. 767-794. 1994.
21. T. Hofmann, Unsupervised learning by probabilistic latent semantic analysis. Machine Learning, 42:177-196. 2001.
22. T. Graepel, M. Burger, K. Obermayer, Self-organizing maps: generalizations and new optimization techniques, Neurocomputing 21, pp.173-190.1998.
23. T. Heskes, Self-organizing maps, vector quantization, and mixture modeling, IEEE Trans. NeuralNetworks 12, pp. 1299-1305. 2001.
24. C.L. Blake and C.L Merz "UCI repository of machine learning databases". Technical report. University of California, Department of information and Computer science, Irvine, CA, available at: ftp://ftp.ics. uci.edu/pub/machine-learning-databases. 1998.