Independent Component Analysis and Extensions with Noise and Time: A Bayesian Ying-Yang Learning Perspective
نویسنده
چکیده
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 superGaussians 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.
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