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Inverting Monotonic Nonlinearities by Entropy Maximization

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dc.contributor Universitat de Vic - Universitat Central de Catalunya. Grup de Recerca en Tractament de Dades i senyals
dc.contributor.author Solé-Casals, Jordi
dc.contributor.author Lopez-de-Ipiña, Karmele
dc.contributor.author Caiafa, Cesar F.
dc.date.accessioned 2017-05-15T12:36:43Z
dc.date.available 2017-05-15T12:36:43Z
dc.date.created 2016
dc.date.issued 2016
dc.identifier.citation Solé-Casals, J., López-de-Ipiña Pena, K., Caiafa, C.F. (2016) Inverting Monotonic Nonlinearities by Entropy Maximization. PLoS ONE 11(10): e0165288. es
dc.identifier.issn 1932-6203
dc.identifier.uri http://hdl.handle.net/10854/4995
dc.description.abstract This paper proposes a new method for blind inversion of a monotonic nonlinear map applied to a sum of random variables. Such kinds of mixtures of random variables are found in source separation and Wiener system inversion problems, for example. The importance of our proposed method is based on the fact that it permits to decouple the estimation of the nonlinear part (nonlinear compensation) from the estimation of the linear one (source separation matrix or deconvolution filter), which can be solved by applying any convenient linear algorithm. Our new nonlinear compensation algorithm, the MaxEnt algorithm, generalizes the idea of Gaussianization of the observation by maximizing its entropy instead. We developed two versions of our algorithm based either in a polynomial or a neural network parameterization of the nonlinear function. We provide a sufficient condition on the nonlinear function and the probability distribution that gives a guarantee for the MaxEnt method to succeed compensating the distortion. Through an extensive set of simulations, MaxEnt is compared with existing algorithms for blind approximation of nonlinear maps. Experiments show that MaxEnt is able to successfully compensate monotonic distortions outperforming other methods in terms of the obtained Signal to Noise Ratio in many important cases, for example when the number of variables in a mixture is small. Besides its ability for compensating nonlinearities, MaxEnt is very robust, i.e. showing small variability in the results. 1. es
dc.format.extent 17 p. es
dc.language.iso eng es
dc.publisher Plos One es
dc.rights Aquest document està subjecte a una llicència Creative Commons es
dc.rights.uri http://creativecommons.org/licenses/by/4.0/ es
dc.subject.other Tractament del senyal es
dc.title Inverting Monotonic Nonlinearities by Entropy Maximization es
dc.type info:eu-repo/semantics/article es
dc.identifier.doi http://dx.doi.org/10.1371/journal.pone.0165288
dc.rights.accesRights info:eu-repo/semantics/openAccess es
dc.type.version info:eu-repo/publishedVersion es
dc.indexacio Indexat a WOS/JCR es
dc.indexacio Indexat a SCOPUS es

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