Bayesian Source Separation of Linear and Linear-quadratic Mixtures Using Truncated Priors

dc.creatorDuarte, LT
dc.creatorJutten, C
dc.creatorMoussaoui, S
dc.date2011
dc.dateDEC
dc.date2014-07-30T13:51:44Z
dc.date2015-11-26T17:09:49Z
dc.date2014-07-30T13:51:44Z
dc.date2015-11-26T17:09:49Z
dc.date.accessioned2018-03-28T23:58:27Z
dc.date.available2018-03-28T23:58:27Z
dc.identifierJournal Of Signal Processing Systems For Signal Image And Video Technology. Springer, v. 65, n. 3, n. 311, n. 323, 2011.
dc.identifier1939-8018
dc.identifierWOS:000296798200004
dc.identifier10.1007/s11265-010-0488-3
dc.identifierhttp://www.repositorio.unicamp.br/jspui/handle/REPOSIP/55355
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/55355
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1280717
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionIn this work, we propose a Bayesian source separation method of linear-quadratic (LQ) and linear mixtures. Since our method relies on truncated prior distributions, it is particularly useful when the bounds of the sources and of the mixing coefficients are known in advance; this is the case, for instance, in non-negative matrix factorization. To implement our idea, we consider a Gibbs' sampler equipped with latent variables, which are set to simplify the sampling steps. Experiments with synthetic data point out that the new proposal performs well in situations where classical ICA-based solutions fail to separate the sources. Moreover, in order to illustrate the application of our method to actual data, we consider the problem of separating scanned images.
dc.description65
dc.description3
dc.description311
dc.description323
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.languageen
dc.publisherSpringer
dc.publisherNew York
dc.publisherEUA
dc.relationJournal Of Signal Processing Systems For Signal Image And Video Technology
dc.relationJ. Signal Process. Syst. Signal Image Video Technol.
dc.rightsfechado
dc.rightshttp://www.springer.com/open+access/authors+rights?SGWID=0-176704-12-683201-0
dc.sourceWeb of Science
dc.subjectSource separation
dc.subjectBayesian approach
dc.subjectNonlinear mixtures
dc.subjectTruncated priors
dc.subjectScanned images
dc.subjectDocuments
dc.titleBayesian Source Separation of Linear and Linear-quadratic Mixtures Using Truncated Priors
dc.typeArtículos de revistas


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