An expectation-maximization (EM) algorithm for independent component analysis in the presence of gaussian noise is presented. The estimation of the conditional moments of the source posterior can be accomplished by maximum a posteriori estimation. The approximate conditional moments enable the development of an EM algorithm for inferring the most probable sources and learning the parameter in noisy independent component analysis. Simulation results show that the proposed method can perform blind source separation of sub-Gaussian mixtures and super-Gaussian mixtures.
|Number of pages||7|
|Journal||Neural Information Processing - Letters and Reviews|
|Publication status||Published - Jan 2004|