title: MISEP - Linear and Nonlinear ICA Based on Mutual Information creator: Almeida, Luis B. subject: Statistical Models subject: Machine Learning subject: Artificial Intelligence description: MISEP is a method for linear and nonlinear ICA, that is able to handle a large variety of situations. It is an extension of the well known INFOMAX method, in two directions: (1) handling of nonlinear mixtures, and (2) learning the nonlinearities to be used at the outputs. The method can therefore separate linear and nonlinear mixtures of components with a wide range of statistical distributions. This paper presents the basis of the MISEP method, as well as experimental results obtained with it. The results illustrate the applicability of the method to various situations, and show that, although the nonlinear blind separation problem is ill-posed, use of regularization allows the problem to be solved when the nonlinear mixture is relatively smooth. publisher: Elsevier contributor: Oja, Erkki contributor: Harmeling, Stefan contributor: Almeida, Luis B. date: 2003 type: Journal (On-line/Unpaginated) type: NonPeerReviewed format: application/pdf identifier: http://cogprints.org/2856/1/AlmeidaSigProc2003.pdf identifier: Almeida, Luis B. (2003) MISEP - Linear and Nonlinear ICA Based on Mutual Information. [Journal (On-line/Unpaginated)] (Unpublished) relation: http://cogprints.org/2856/