TY - UNPB ID - cogprints2691 UR - http://cogprints.org/2691/ A1 - Almeida, Luis B. TI - Faster Training in Nonlinear ICA using MISEP Y1 - 2002/// N2 - MISEP has been proposed as a generalization of the INFOMAX method in two directions: (1) handling of nonlinear mixtures, and (2) learning the nonlinearities to be used at the outputs, making the method suitable to the separation of components with a wide range of statistical distributions. In all implementations up to now, MISEP had used multilayer perceptrons (MLPs) to perform the nonlinear ICA operation. Use of MLPs sometimes leads to a relatively slow training. This has been attributed, at least in part, to the non-local character of the MLP's units. This paper investigates the possibility of using a network of radial basis function (RBF) units for performing the nonlinear ICA operation. It shows that the local character of the RBF network's units allows a significant speedup in the training of the system. The paper gives a brief introduction to the basics of the MISEP method, and presents experimental results showing the speed advantage of using an RBF-based network to perform the ICA operation. AV - public KW - Independent components analysis KW - nonlinear KW - blind source separation KW - ICA KW - BSS ER -