288 pp. per issue
6 x 9, illustrated
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Neural Computation

August 2016, Vol. 28, No. 8, Pages 1663-1693
(doi: 10.1162/NECO_a_00853)
© 2016 Massachusetts Institute of Technology
A Quasi-Likelihood Approach to Nonnegative Matrix Factorization
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A unified approach to nonnegative matrix factorization based on the theory of generalized linear models is proposed. This approach embeds a variety of statistical models, including the exponential family, within a single theoretical framework and provides a unified view of such factorizations from the perspective of quasi-likelihood. Using this framework, a family of algorithms for handling signal-dependent noise is developed and its convergence proved using the expectation-maximization algorithm. In addition, a measure to evaluate the goodness of fit of the resulting factorization is described. The proposed methods allow modeling of nonlinear effects using appropriate link functions and are illustrated using an application in biomedical signal processing.