• DocumentCode
    327753
  • Title

    MIXFIT: an algorithm for the automatic fitting and testing of normal mixture models

  • Author

    McLachlan, G.J. ; Peel, D.

  • Author_Institution
    Dept. of Math., Queensland Univ., St. Lucia, Qld., Australia
  • Volume
    1
  • fYear
    1998
  • fDate
    16-20 Aug 1998
  • Firstpage
    553
  • Abstract
    We consider the fitting of normal mixture models to multivariate data, using maximum likelihood via the EM algorithm. This approach requires the specification of all initial estimate of the vector of unknown parameters, or equivalently, of an initial classification of the data with respect to the components of the mixture model underfit. We describe an algorithm called MIXFIT that automatically undertakes this fitting, including the specification of suitable initial values if not supplied by the user The MIXFIT algorithm has several options, including the provision to carry out a resampling-based test for the number of components in the mixture model
  • Keywords
    maximum likelihood estimation; pattern recognition; EM algorithm; MIXFIT; initial data classification; maximum likelihood methods; model fitting; model testing; multivariate data; normal mixture models; resampling-based test; unknown parameter vector; Algorithm design and analysis; Automatic testing; Clustering algorithms; Covariance matrix; Iterative algorithms; Mathematical model; Mathematics; Maximum likelihood estimation; Shape; Statistics;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Pattern Recognition, 1998. Proceedings. Fourteenth International Conference on
  • Conference_Location
    Brisbane, Qld.
  • ISSN
    1051-4651
  • Print_ISBN
    0-8186-8512-3
  • Type

    conf

  • DOI
    10.1109/ICPR.1998.711203
  • Filename
    711203