Gaussian moments for noisy unifying model

A unifying model that combines three properties is proposed by Hyvärinen, and a gradient ascent algorithm for independent component analysis (ICA) is performed by maximum likelihood estimation. In this paper, we consider the estimation of the data model of ICA when Gaussian noise is present and the independent components are time dependent. Firstly, according to the useful property of Gaussian moments, we introduce Gaussian moments algorithm to estimation of the noisy unifying model when the noise covariance matrix is known. Next, when the noise covariance is unknown, a new Gaussian moments algorithm is developed. Finally, the validity and performance of our algorithms are demonstrated by computer simulations.