Data driven constraints for Gaussian mixtures of factor analyzers: an application to market segmentation
Francesca Greselin, Salvatore Ingrassia · BOA (University of Milano-Bicocca) · 2013
We want to make a first explorative analysis on traffic usage for a telecom company, and further we employ mixtures of factor analyzers, estimated through EM to model the data. As the maximization of the log-likelihood without any constraint is an ill-posed problem (Day, 1969) to reduce spurious local maximizers and avoid singularities, some authors propose to take a common (diagonal) error matrix (MCFA Baek et al., 2010) or to impose an isotropic error matrix (Bishop and Tippin, 1998). Our proposal is here to employ a less constrained approach, based on covariance decomposition. A first application is shown, suggesting a non-unique behavior of customers inside the traffic plan, which could be used for further marketing analyses.