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deville y nonlinear blind source separation blind mixture identification 2021

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Textbook in PDF format This book provides a detailed survey of the methods that were recently developed to handle advanced versions of the blind source separation problem, which involve several types of nonlinear mixtures. Another attractive feature of the book is that it is based on a coherent framework. More precisely, the authors first present a general procedure for developing blind source separation methods. Then, all reported methods are defined with respect to this procedure. This allows the reader not only to more easily follow the description of each method but also to see how these methods relate to one another. The coherence of this book also results from the fact that the same notations are used throughout the chapters for the quantities (source signals and so on) that are used in various methods. Finally, among the quite varied types of processing methods that are presented in this book, a significant part of this description is dedicated to methods based on artificial neural networks, especially recurrent ones, which are currently of high interest to the data analysis and machine learning community in general, beyond the more specific signal processing and blind source separation communities. Preface Acknowledgments Introduction Expressions and Variants of the Linear-Quadratic Mixing Model Scalar Form of Mixing Model Matrix-Vector Forms of Mixing Model Overall Matrix Form of Mixing Model Determined Mixtures of Original or Extended Sources Invertibility of Mixing Model, Separating Structures A Linear Separating Structure for Determined Mixtures of Extended Sources Analytical Inversion of Determined Mixtures of Original Sources Bilinear Mixture of Two Original Sources More General Linear-Quadratic Mixtures General-Purpose Numerical Inversion of Determined Mixtures of Original Sources Dedicated Nonlinear Recurrent Neural Networks for Determined Mixtures of Original Sources Revisiting Linear Mixtures Bilinear Mixture of Two Original Sources Other Linear-Quadratic Mixtures Fitting the Direct Model Independent Component Analysis and Bayesian SeparationMethods Methods for i.i.d. Sources Exploiting the Mutual Independence of the Outputs of a Separating System A Moment-Based Method An Approach Based on Mutual Information Methods Focused on Estimating the Mixing Model The Maximum Likelihood Approach Methods Based on Cumulants and/or Moments Jointly Estimating the Sources and Mixing Model Methods for Non-i.i.d. Sources Methods Focused on Estimating the Sources or Mixing Model The Maximum Likelihood Approach Moment-Based Methods Bayesian Method Matrix Factorization Methods General Features and Separation Principle Methods Without Structural Constraints Methods with Constrained Source Variables Methods with Nonnegativity (and Other) Constraints Methods Without Nonnegativity Constraints Methods with Constrained Source and Mixture Variables Methods Without Nonnegativity Constraints Methods with Nonnegativity Constraints Sparse Component Analysis Methods A Method Based on L0 pseudo-Norm Methods Based on Single-Source Zones Extensions and Conclusion Bilinear Sparse Component Analysis Methods Based on Single-Source Zones Considered Signals Definitions and Assumptions, Sparsity Concepts SCA Methods Identification of Linear Part of Mixture Cancellation of Linear Part of Mixture Remaining BMI and BSS Tasks A Method Based on Non-stationarity Conditions A Method Also Using Other Correlation Parameters A Method Only Using Variance Parameters Bibliography Index

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deville y nonlinear blind source separation blind mixture identification 2021

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