The convergence analysis of the complex ICA algorithms using symmetric orthogonalization

The contrast function optimization based independent component analysis is one of the most widespread methods in separating independent sources from their linear mixtures. The convergence analysis of such algorithms, which has both theoretical and practical value, is a current research focus. In this article, we present a convergence analysis for the separation of complex sources via use of these algorithms. Based on this analysis, we provide the characterization of the stationary points of the algorithms using symmetric orthogonalization.