Sequentially greedy unsupervised learning of gaussian mixture models by means of a binary tree structure

Intelligent Autonomous Systems 11, IAS 2010

Volumn , Issue , 2010, Pages 35-46

  Greggio, Nicola ^a   Bernardino, Alexandre ^b   Santos Victor, José ^b  

^a SCUOLA SUPERIORE SANT ANNA   (Italy)

^b INSTITUTE FOR SYSTEMS AND ROBOTICS   (Portugal)

Author keywords

Machine learning; Self adapting expectation maximization; Unsupervised clustering

Indexed keywords

BINARY TREE STRUCTURE; COMPUTATIONAL BURDEN; DATA FITS; DATA SETS; EXPECTATION MAXIMIZATION; GAUSSIAN MIXTURE MODEL; ILL POSED PROBLEM; INFORMATION CRITERION; MIXTURE COMPONENTS; MIXTURE MODEL; NUMBER OF COMPONENTS; REAL IMAGES; UNSUPERVISED CLUSTERING;

LEARNING SYSTEMS; MAXIMUM PRINCIPLE; UNSUPERVISED LEARNING;

LEARNING ALGORITHMS;

EID: 84871632188     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.3233/978-1-60750-613-3-35     Document Type: Conference Paper

References (20)

1. T. Kohonen, "Analysis of a simple self-organizing process." Biological Cybernetics, vol. 44, no. 2, pp. 135-140, 1982.
2. B. Fritzke, "A growing neural gas network learns topologies." Advances in Neural Inform ation Processing Systems 7 (NIPS'94), MIT Press, Cambridge MA, pp. 625-632, 1995.
3. J. B. MacQueen, "Some methods for classification and analysis of multivariate observations." Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability., pp. 281-297, 1967.
4. P. Comon, "Independent component analysis: a new concept?" Signal Processing, Elsevier, vol. 36, no. 3, pp. 287-314, 1994.
5. A. Hyvärinen, J. Karhunen, and E. Oja, "Independent component analysis, " New York: John Wiley and Sons, vol. ISBN 978-0-471-40540-5, 2001.
6. G. McLachlan and D. Peel, "Finite mixture models." John Wiley and Sons, 2000.
7. G. McLachlan and K. T., "The em algorithm and extensions, " New York: John Wiley and Sons, 1997.
8. A. Dempster, N. Laird, and D. Rubin, "Maximum likelihood estimation from incomplete data via the em algorithm, " J. Royal Statistic Soc., vol. 30, no. B, pp. 1-38, 1977.
9. L. Xu and J. M., "On convergence properties of the em algorithm for gaussian mixtures, " Neural Computation, vol. 8, pp. 129-151, 1996.
10. Y. Sakimoto, M. Iahiguro, and G. Kitagawa, "Akaike information criterion statistics, " KTK Scientific Publisher, Tokio, 1986.
11. G. Schwarz, "Estimating the dimension of a model, " Ann. Statist., vol. 6, no. 2, pp. 461-464, 1978.
12. J. Rissanen, "Stochastic complexity in statistical jnquiry." Wold Scientific Publishing Co. USA, 1989.
13. C. Wallace and P. Freeman, "Estimation and inference by compact coding, " J. Royal Statistic Soc. B, vol. 49, no. 3, pp. 241-252, 1987.
14. F. Pernkopf and D. Bouchaffra, "Genetic-based em algorithm for learning gaussian mixture models, " IEEE Trans. Patt. Anal. Mach. Intell., vol. 27, no. 8, pp. 1344-1348, 2005.
15. N. Vlassis and A. Likas, "A greedy em algorithm for gaussian mixture learning, " Neural Processing Letters, vol. 15, pp. 77-87, 2002.
16. J. Verbeek, N. Vlassis,, and B. Krose, "Efficient greedy learning of gaussian mixture models, " Neural Computation, vol. 15, no. 2, pp. 469-485, 2003.
17. A. Lanterman, "Schwarz, wallace and rissanen: Intertwining themes in theories of model order estimation, " Int'l Statistical Rev., vol. 69, pp. 185-212, 2001.
18. A. Figueiredo and A. Jain, "Unsupervised learning of finite mixture models, " IEEE Trans. Patt. Anal. Mach. Intell., vol. 24, no. 3, 2002.
19. J. H. Jensen, D. Ellis, M. G. Christensen, and S. H. Jensen, "Evaluation distance measures between gaussian mixture models of mfccs, " Proc. Int. Conf. on Music Info. Retrieval ISMIR-07 Vienna, Austria, pp. 107-108, October, 2007.
20. P. Ahrendt, "The multivariate gaussian probability distribution, " http://www2.imm.dtu.dk/pubdb/p.php?3312, Tech. Rep., 2005.