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Proportional data modeling via selection and estimation of a finite mixture of scaled Dirichlet distributions
Author(s) -
Zamzami Nuha,
Alsuroji Rua,
Eromonsele Oboh,
Bouguila Nizar
Publication year - 2020
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/coin.12246
Subject(s) - dirichlet distribution , generalized dirichlet distribution , computer science , mathematics , latent dirichlet allocation , flexibility (engineering) , model selection , selection (genetic algorithm) , minimum description length , algorithm , artificial intelligence , statistics , topic model , dirichlet's principle , mathematical analysis , boundary value problem
This paper proposes an unsupervised algorithm for learning a finite mixture of scaled Dirichlet distributions. Parameters estimation is based on the maximum likelihood approach, and the minimum message length (MML) criterion is proposed for selecting the optimal number of components. This research work is motivated by the flexibility issues of the Dirichlet distribution, the widely used model for multivariate proportional data, which has prompted a number of scholars to search for generalizations of the Dirichlet. By introducing the extra parameters of the scaled Dirichlet, several useful statistical models could be obtained. Experimental results are presented using both synthetic and real datasets. Moreover, challenging real‐world applications are empirically investigated to evaluate the efficiency of our proposed statistical framework.