title: Inverting Monotonic Nonlinearities by Entropy Maximization.
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.
authors: Data and Signal Processing Research Group, U Science Tech, University of Vic - Central University of Catalonia, Vic, Catalonia, Spain.
authors: Jordi
authors: http://orcid.org/0000-0002-6534-1979
authors: J
authors: Solé-Casals
authors: Systems Engineering and Automation Department, Universidad del País Vasco/Euskal Herriko Unibertsitatea, EleKin Research Group, Donostia, Spain.
authors: Karmele
authors: K
authors: López-de-Ipiña Pena
authors: Instituto Argentino de Radioastronomía (IAR), CONICET, CCT - La Plata, Buenos Aires, Argentina.
authors: Cesar F
authors: CF
authors: Caiafa
mesh_terms: D000465:Algorithms; D019277:Entropy; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D016011:Normal Distribution; D059629:Signal-To-Noise Ratio