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Mixtures of Gaussian factors are powerful tools for modeling an unobservedheterogeneous population, offering - at the same time - dimension reduction andmodel-based clustering. Unfortunately, the high prevalence of spurioussolutions and the disturbing effects of outlying observations, along maximumlikelihood estimation, open serious issues. In this paper we considerrestrictions for the component covariances, to avoid spurious solutions, andtrimming, to provide robustness against violations of normality assumptions ofthe underlying latent factors. A detailed AECM algorithm for this new approachis presented. Simulation results and an application to the AIS dataset show theaim and effectiveness of the proposed methodology.

Robust estimation for mixtures of Gaussian factor analyzers, based on trimming and constraints