Titlebook: Nonlinear Blind Source Separation and Blind Mixture Identification; Methods for Bilinear Yannick Deville,Leonardo Tomazeli Duarte,Shahram H

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Invertibility of Mixing Model, Separating Structures,ear and purely quadratic restricted versions), which were defined in the previous chapter. Various structures may be used to this end, including nonlinear recurrent artificial neural networks. Part of these structures were derived by extending previously reported structures for linear mixtures. Both

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Independent Component Analysis and Bayesian Separation Methods, (including their bilinear and purely quadratic restricted versions), namely methods strongly based on a probabilistic framework. This mainly includes not only nonlinear versions of Independent Component Analysis (ICA) but also Bayesian methods. The description of these methods is split into two par

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Matrix Factorization Methods,s (including their bilinear and purely quadratic restricted versions), namely methods based on matrix factorization. Various versions of these methods are described, depending on the considered constraints. Some of these constraints concern the source values and/or mixing coefficients, e.g., when co

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Extensions and Conclusion,ic mixtures (including their restricted versions), we here extend this overview to the main trends that may be observed in methods suited to more general mixtures, namely higher-order polynomial ones, which have been addressed in a few reported investigations, e.g., to handle multiple (beyond second

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Yannick Deville,Leonardo Tomazeli Duarte,Shahram Hosseini that students should have a command of the content and processes of science has been in place for most of this century. What occurred as the century progressed was an expansion of the goal to include all students. In the 1950s, Hurd’s essay captured the essence of the need for a scientifically educ

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