Abstract
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.
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Bouguila, N., Ziou, D. (2005). On Fitting Finite Dirichlet Mixture Using ECM and MML. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds) Pattern Recognition and Data Mining. ICAPR 2005. Lecture Notes in Computer Science, vol 3686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11551188_19
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DOI: https://doi.org/10.1007/11551188_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28757-5
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