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
Link To Document