A macro-DAG structure based mixture model

Bernard Chalmond (1)

1 CMLA - Centre de Mathématiques et de Leurs Applications

In the context of unsupervised classification of multidimensional data, we revisit the classical mixture model in the case where the dependencies among the random variables are described by a DAG structure. The structure is considered at two levels, the original DAG and its macro-representation. This two-level representation is the main base of the proposed mixture model. To perform unsupervised classification, we propose a dedicated algorithm called EM-mDAG, which extends the classical EM algorithm. In the Gaussian case, we show that this algorithm can be efficiently implemented. The experiments reveal that this method favors the selection of a small number of classes.

Mots clés: Mixture model; DAG structure; Bayesian network; EM algorithm

Domaines: Statistiques [math.ST]; Théorie [stat.TH]

Bernard Chalmond. A macro-DAG structure based mixture model. 2013. ⟨hal-00947454⟩