Title: Effect of penalisation on a mixture of factor analysers
Authors: Nam-Hwui Kim - University of Waterloo (Canada) [presenting]
Ryan Browne - University of Waterloo (Canada)
Abstract: Factor analysers can be used to obtain a parsimonious estimate of component-wise covariance matrices in a finite mixture model. In addition, one could achieve further parsimony in estimated covariance matrix by penalising on the factor loading matrix. However, an increasing magnitude of penalisation coefficient may result in degenerate factor loading estimates, which may have an adverse effect on maximum likelihood estimation of model parameters. To this end, we investigate the effect of penalisation on sparse estimation of parameters in a finite mixture of factor analysers. We also investigate the effect of such estimates in model-based clustering settings.