Linear and nonlinear ICA based on mutual information

In the context of independent components analysis (ICA), the mutual information (MI) of the extracted components is one of the most desirable measures of independence, due to its special properties. This paper presents a method for performing linear and nonlinear ICA based on MI, with few approximations. The use of MI as an objective function for ICA requires the estimation of the statistical distributions of the separated components. In this work, both the extraction of independent components and the estimation of their distributions are performed simultaneously, by a single network with a specialized structure, trained with a single objective function.