Bayesian Independent Component Analysis: Variational methods and non-negative decompositions

Ole Winther, Kaare Brandt Petersen

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    Abstract

    In this paper we present an empirical Bayesian framework for independent component analysis. The framework provides estimates of the sources, the mixing matrix and the noise parameters, and is flexible with respect to choice of source prior and the number of sources and sensors. Inside the engine of the method are two mean field techniques-the variational Bayes and the expectation consistent framework-and the cost function relating to these methods are optimized using the adaptive overrelaxed expectation maximization (EM) algorithm and the easy gradient recipe. The entire framework, implemented in a Matlab toolbox, is demonstrated for non-negative decompositions and compared with non-negative matrix factorization.
    Original languageEnglish
    JournalDigital Signal Processing
    Volume17
    Issue number5
    Pages (from-to)858-872
    ISSN1051-2004
    DOIs
    Publication statusPublished - 2007

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