Abstract

Modeling data with Gaussian distributions is an important statistical problem. To obtain robust models one imposes constraints the means and covariances of these distributions [6, 4, 10, 8]. Constrained ML modeling implies the existence of optimal feature spaces where the constraints are more valid [2, 3]. This paper introduces one such constrained ML modeling technique called factor analysis invariant to linear transformations (FACILT) which is essentially factor analysis in optimal feature spaces. FACILT is a generalization of several existing methods for modeling covariances. This paper presents an EM algorithm for FACILT modeling.

Citations