Independent Component Analysis and Extensions with Noise and Time: A Bayesian Ying-Yang Learning Perspective
Lei Xu · 2003
Abstract — After summarizing typical approaches for solving independent component analysis (ICA) problems, advances on the ICA studies that consider hybrid sources of both subGaussians and super-Gaussians and the ICA extensions that consider noise and temporal dependence among observations have been overviewed from the perspective of Bayesian Ying-Yang independence learning. Not only new insights are provided on existing results in literature, but also a number of further results are presented.