On Fitting Finite Dirichlet Mixture Using ECM and MML

On Fitting Finite Dirichlet Mixture Using ECM and MML

Gaussian mixture models are being increasingly used in pattern recognition applications. However, for a set of data other distributions can give better results. In this paper, we consider Dirichlet mixtures which offer many advantages [1]. The use of the ECM algorithm and the minimum message length...

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Hauptverfasser: Bouguila, Nizar, Ziou, Djemel
Format: Tagungsbericht
Sprache:eng
Schlagworte:
Applied sciences
Computer science
control theory
systems
Confusion Matrix
Data processing. List processing. Character string processing
Exact sciences and technology
Fisher Information
Gaussian Mixture Model
Hessian Matrix
Information systems. Data bases
Memory organisation. Data processing
Mixture Model
Software
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Beschreibung
Zusammenfassung:Gaussian mixture models are being increasingly used in pattern recognition applications. However, for a set of data other distributions can give better results. In this paper, we consider Dirichlet mixtures which offer many advantages [1]. The use of the ECM algorithm and the minimum message length (MML) approach to fit this mixture model is described. Experimental results involve the summarization of texture image databases.
ISSN:0302-9743
1611-3349
DOI:10.1007/11551188_19