Generalization of the EM algorithm for mixture density estimation

Pattern Recognition Letters

Volumn 19, Issue 2, 1998, Pages 133-140

  Kehtarnavaz, Nasser ^a   Nakamura, Eiji ^b  

^a TEXAS A AND M UNIVERSITY   (United States)

^b AICHI INSTITUTE OF TECHNOLOGY   (Japan)

Author keywords

Expectation maximization; Mixture density estimation; Model selection; Multi scale clustering

Indexed keywords

ALGORITHMS; ESTIMATION; MATHEMATICAL MODELS; PROBABILITY DENSITY FUNCTION;

EXPECTATION-MAXIMIZATION;

PATTERN RECOGNITION;

EID: 0031995997     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0167-8655(97)00173-6     Document Type: Article

References (10)

1. Bishop, C., 1996. Neural Networks for Pattern Recognition. Oxford University Press, Oxford, UK.
2. Box M. A new method of constrained optimization and a comparison with other methods. Computer J. 8:1965;42-52.
3. Dempster A., Laird N., Rubin D. Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Statist. Soc. Ser. B. 39:1977;1-38.
4. Duda, R., Hart, P., 1973. Pattern Classification and Scene Analysis. John Wiley & Sons, New York.
5. Haykin, S., 1994. Neural Networks. A comprehensive Foundation. IEEE Press, NJ.
6. Lindeberg, T., 1994. Scale-Space Theory in Computer Vision. Kluwer Academic Publishers, Dordrecht, The Netherlands.
7. Nakamura, E., 1996. Clustering based on scale-space theory. Ph.D. Dissertation. Department of Electrical Engineering, Texas A& M University, College Station, TX.
8. Redner R.A., Walker H.F. Mixture densities, maximum likelihood and the EM algorithm. SIAM Review. 26:1984;195-239.
9. Richardson S., Green P.J. On bayesian analysis of mixtures with an unknown number of components. J. Roy. Statist. Soc. Ser. B. 59:1977;731-792.
10. Titterington, D., Smith, A., Makov, U., 1985. Statistical Analysis of Finite Mixture Distributions. John Wiley & Sons, New York.