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.
Greselin, F., Ingrassia, S. (2013). Data driven constraints for Gaussian mixtures of factor analyzers: an application to market segmentation. Intervento presentato a: Arbeitsgruppe Datenanalyse und numerische Klassifikation (AD DANK) and British Classification Society (BCS) Meeting, University College London.
Data driven constraints for Gaussian mixtures of factor analyzers: an application to market segmentation
GRESELIN, FRANCESCA;
2013
Abstract
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.
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