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
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;
Conference_Titel :
Pattern Recognition, 1998. Proceedings. Fourteenth International Conference on
Conference_Location :
Brisbane, Qld.
Print_ISBN :
0-8186-8512-3
DOI :
10.1109/ICPR.1998.711203