An entropy maximization approach to optimal model selection in gaussian mixtures

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

Volumn 2905, Issue , 2003, Pages 432-439

  Peñalver, Antonio ^a   Sáez, Juan M ^a   Escolano, Francisco ^a  

^a UNIVERSITY OF ALICANTE   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; ENTROPY; GAUSSIAN DISTRIBUTION; IMAGE SEGMENTATION; MAXIMUM LIKELIHOOD; MAXIMUM PRINCIPLE; PARAMETER ESTIMATION; PATTERN RECOGNITION; PRINCIPAL COMPONENT ANALYSIS;

ENTROPY MAXIMIZATION; EXPECTATION - MAXIMIZATIONS; FIXED NUMBERS; GAUSSIAN MIXTURE MODEL; GAUSSIAN MIXTURES; OPTIMAL NUMBER; PDF(PROBABILITY DENSITY FUNCTION); SYNTHETIC AND REAL DATA;

PROBABILITY DENSITY FUNCTION;

EID: 35248876744     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-24586-5_53     Document Type: Article

References (17)

1. Bishop, C.: Neural Networks for Pattern Recognition. Oxford Univ. Press (1995).
2. Blake, C.L, Merz, C.J.:UCI repository of machine learning databases. University of California, Irvine, Dept. of Information and Computer Sciences (1998).
3. Cover, T, Thomas, J: Elements of Information Theory. J. Wiley and Sons (1991).
4. Dempster, A, Laird, N, Rubin, D.: Maximum Likelihood estimation from incomplete data via the EM Algorithm. Journal of The Royal Statistical Society B, vol. 39 pp. 1-38 (1977).
5. Figueiredo, M.A.T, Leitao, J.M.N, Jain, A.K.: On Fitting Mixture Models. In: Hancock, E.R., Pelillo, M. (eds.): Energy Minimization Methods in Computer Vision and Pattern Recognition. Lecture Notes in Computer Science, Vol. 1654. Springer-Verlag, Berlin Heidelberg New York (1999) 54-69.
6. Figueiredo, J.M.N, Jain, A.K.: Unsupervised Selection and Estimation of Finite Mixture Models. In Proceedings of the International Conference on Pattern Recognition. ICPR2000 (Barcelona), 2000.
7. Parzen, E. On estimation of a probability density function and mode. Annals of Mathematical Statistics, 33:1065-1076, 1962.
8. Redner, R.A, Walker, H.F.: Mixture Densities, Maximum Likelihood, and the EM Algorithm. SIAM Review, 26(2):195-239, (1984).
9. Rissanen, J.: Stochastic Complexity in Statistical Inquiry. World Scientific, (1989).
10. Shannon, C.: A Mathematical Theory of Comunication. The Bell System Technical Journal, Vol. 27, 379-423, 623-656, (1948).
11. Ueda, N, Nakano, R, Ghahramani, Z., G. E. Hinton: SMEM Algorithm for Mixture Models. Neural Computation, 12:2109-2128, (2000).
12. Viola, P, Wells III, W. M.: Alignment by Maximization of Mutual Information. In 5th Intern. Conf. on Computer Vision, pages 16-23, Cambridge, MA. IEEE. (1995).
13. Viola, P, Schraudolph, N.N, Sejnowski, T.J.: Empirical Entropy Manipulation for Real-World Problems. Adv. in Neural Infor. Proces. Systems 8, MIT Press (1996).
14. Vlassis, N, Likas, A.: A Kurtosis-Based Dynamic Approach to Gaussian Mixture Modeling. IEEE Trans. Systems, Man, and Cybernetics, 29(4):393-399 (1999).
15. Vlassis, N, Likas, A, Krose, B.: A Multivariate Kurtosis-Based Dynamic Approach to Gaussian Mixture Modeling.Inteligent Autonomous Systems Tech. Report (2000).
16. Vlassis, N, Likas, A.: A Greedy EM Algorithm for Gaussian Mixture Learning. Neural Processing Letters. To appear.
17. Wolpert, D, Wolf, D.: Estimating Function of Probability Distribution from a Finite Set of Samples. Phisical Review E, Volume 52, No 6 (1995).