Independent component analysis with prior information about the mixing matrix

In the Independent Component Analysis (ICA) problem, a linear transformation of the original statistically independent sources is observed. ICA algorithms usually do not include any prior information about the mixing matrix that models the linear transformation. We investigate in this paper in a general framework how the criterion functions can be modified if a prior information about the entries of the mixing matrix is available. We find that the prior can be nicely introduced in the ICA formulation, so a direct modification of traditional algorithms can be carried out. Including prior information in the learning rule does not only improve convergence properties but also extends the application of ICA techniques to data that do not satisfy exactly ICA assumptions. (C) 2002 Elsevier Science B.V. All rights reserved.