Investigations on non-Gaussian factor analysis

This letter further explores the Bayesian Ying-Yang learning based non-Gaussian factor analysis (NFA) via investigating its key yet analytically intractable factor estimating step. Among the three suggested numerical approaches we empirically show that the so-called iterative fixed posteriori approximation approach is the most optimal, as well as theoretically prove that the iterative fixed posteriori approximation is another type of EM-algorithm, with the proof of its convergence also shown.