Factorized EM Algorithm for Mixture Estimation

  • Ivan Nagy
  • Petr Nedoma
  • Miroslav Kárný
Conference paper


A classical version of the EM algorithm is considered in the paper. Its numerical properties are improved using factorized algorithms for maximization in M step of the algorithm. The results are illustrated on simulated examples.


Mixture Model Numerical Property Normal Mixture Factorize Algorithm Radial Basis Neural Network 


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  1. [1]
    A.P. Dempster, N.M. Lair, and D.B. Rubin, “Maximum-likelihood from incomplete data via the em algorithm”, J.Royal Statist. Soc. Ser. B., vol. 39, 1977.Google Scholar
  2. [2]
    C.F.J. Wu, “On the convergence properties of the em algorithm”, The Annals of Statistics, vol. 11, pp. 95–103, 1983.MathSciNetCrossRefMATHGoogle Scholar
  3. [3]
    R. Redner and H. Walker, “Mixture densities, maximum likelihood and the em algorithm”, SIAM Review, vol. 26, 1984.Google Scholar
  4. [4]
    G.J. Bierman, Factorization Methods for Discrete Sequential Estimation, Academic Press, New York, 1977.MATHGoogle Scholar
  5. [5]
    V. Peterka, “Bayesian approach to system identification”, in Trends and Progress in System Identification, P. Eykhoff, Ed., pp. 239–304. Pergamoll Press, Oxford, 1981.Google Scholar

Copyright information

© Springer-Verlag Wien 2001

Authors and Affiliations

  • Ivan Nagy
    • 1
  • Petr Nedoma
  • Miroslav Kárný
    • 2
  1. 1.FD ČVUTPrague 1Czech Republic
  2. 2.ÚTIA, AV ČRPrague 08Czech Republic

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