Gaussian moments for noisy complexity pursuit

Complexity pursuit is an extension of projection pursuit to time series data and the method is closely related to blind separation of time-dependent source signals and independent component analysis (ICA). 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. We derive a simple algorithm combining Gaussian moments and complexity pursuit for noisy ICA. Validity and performance of the described approaches are demonstrated by computer simulations.