TY - UNPB N1 - Submitted to the JOurnal of Machine Learning Research ID - cogprints2687 UR - http://cogprints.org/2687/ A1 - Almeida, Luis B. TI - MISEP - Linear and Nonlinear ICA Based on Mutual Information Y1 - 2002/12// N2 - Linear Independent Components Analysis (ICA) has become an important signal processing and data analysis technique, the typical application being blind source separation in a wide range of signals, such as biomedical, acoustical and astrophysical ones. Nonlinear ICA is less developed, but has the potential to become at least as powerful. This paper presents MISEP, an ICA technique for linear and nonlinear mixtures, which is based on the minimization of the mutual information of the estimated components. MISEP is a generalization of the popular INFOMAX technique, which is extended in two ways: (1) to deal with nonlinear mixtures, and (2) to be able to adapt to the actual statistical distributions of the sources, by dynamically estimating the nonlinearities to be used at the outputs. The resulting MISEP method optimizes a network with a specialized architecture, with a single objective function: the output entropy. Examples of both linear and nonlinear ICA performed by MISEP are presented in the paper. AV - public KW - Independent components analysis KW - nonlinear KW - blind source separation KW - ICA KW - BSS ER -