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Post-Nonlinear Mixtures and Beyond

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dc.contributor Universitat de Vic. Escola Politècnica Superior
dc.contributor Universitat de Vic. Grup de Recerca en Tecnologies Digitals
dc.contributor World Automation Congress (6è: 2004 : Sevilla)
dc.contributor.author Solé-Casals, Jordi
dc.contributor.author Jutten, Christian
dc.date.accessioned 2013-02-18T09:01:07Z
dc.date.available 2013-02-18T09:01:07Z
dc.date.issued 2004
dc.identifier.citation SOLÉ CASALS, J. and JUTTEN, C., 2004. Post-nonlinear mixtures and beyond, M. JAMSHIDI, L. FOULLOY, A. ELKAMEL and J.S. JAMSHIDI, eds. In: Intelligent Automations and Control: Trends Principles, and Applications, Vol 16; TSI PRESS SERIES; 5th International Symposium on Intelligent Automation and Control/9th International Symposium on Manufacturing and Applications held at the 6th Biannual World Automation Congress, JUN 28-JUL 01, 2004 2004, TSI PRESS, pp. 67-74. ca_ES
dc.identifier.isbn 9781889335223
dc.identifier.uri http://hdl.handle.net/10854/2088
dc.description.abstract Although sources in general nonlinear mixturm arc not separable iising only statistical independence, a special and realistic case of nonlinear mixtnres, the post nonlinear (PNL) mixture is separable choosing a suited separating system. Then, a natural approach is based on the estimation of tho separating Bystem parameters by minimizing an indcpendence criterion, like estimated mwce mutual information. This class of methods requires higher (than 2) order statistics, and cannot separate Gaarsian sources. However, use of [weak) prior, like source temporal correlation or nonstationarity, leads to other source separation Jgw rithms, which are able to separate Gaussian sourra, and can even, for a few of them, works with second-order statistics. Recently, modeling time correlated s011rces by Markov models, we propose vcry efficient algorithms hmed on minimization of the conditional mutual information. Currently, using the prior of temporally correlated sources, we investigate the fesihility of inverting PNL mixtures with non-bijectiw non-liacarities, like quadratic functions. In this paper, we review the main ICA and BSS results for riunlinear mixtures, present PNL models and algorithms, and finish with advanced resutts using temporally correlated snu~sm ca_ES
dc.format application/pdf
dc.format.extent 8 p. ca_ES
dc.language.iso eng ca_ES
dc.publisher TSI Press ca_ES
dc.rights (c) TSI Press, 2004
dc.rights Tots els drets reservats ca_ES
dc.subject.other Robòtica ca_ES
dc.subject.other Control automàtic ca_ES
dc.title Post-Nonlinear Mixtures and Beyond ca_ES
dc.type info:eu-repo/semantics/conferenceObject ca_ES
dc.rights.accesRights info:eu-repo/semantics/openAccess ca_ES

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