An iterative projections algorithm for ML factor analysis

Abstract

Alternating minimization of the information divergence is used to derive an effective algorithm for maximum likelihood (ML) factor analysis. The proposed algorithm is derived as an iterative alternating projections procedure on a model family of probability distributions defined on the factor analysis model and a desired family of probability distributions constrained to be concentrated on the observed data. The algorithm presents the advantage of being simple to implement and stable to converge. A simulation example that illustrates the effectiveness of the proposed algorithm for ML factor analysis is presented.