The Maximum Likelihood Approach to Complex ICA

We derive the form of the best non-linear functions for performing independent component analysis (ICA) by maximum likelihood estimation. We show that both the form of nonlinearity and the relative gradient update equations for likelihood maximization naturally generalize to the complex case, and that they coincide with the real case. We discuss several special cases for the score function as well as adaptive scores.