Mixture of factor analyzers for mixed-type data via a composite likelihood approach

A parsimonious modelling approach for clustering mixed-type (ordinal and continuous) data is presented. It is assumed that ordinal and continuous data follow a finite mixture of Gaussians that is only partially observed.We define a general class of parsimonious models for mixed-type data by imposing a factor decomposition on component-specific covariance matrices. Parameter estimation is carried out using a EM-type algorithm based on composite likelihood.