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Nonlinear blind source separation an...

Deville, Yannick.

Nonlinear blind source separation and blind mixture identificationmethods for bilinear, linear-quadratic and polynomial mixtures /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Nonlinear blind source separation and blind mixture identificationby Yannick Deville, Leonardo Tomazeli Duarte, Shahram Hosseini.
Reminder of title:	methods for bilinear, linear-quadratic and polynomial mixtures /
Author:	Deville, Yannick.
other author:	Duarte, Leonardo Tomazeli.
Published:	Cham :Springer International Publishing :2021.
Description:	ix, 71 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Blind source separation.
Online resource:	https://doi.org/10.1007/978-3-030-64977-7
ISBN:	9783030649777$q(electronic bk.)

Nonlinear blind source separation and blind mixture identificationmethods for bilinear, linear-quadratic and polynomial mixtures /
Deville, Yannick.

Nonlinear blind source separation and blind mixture identificationmethods for bilinear, linear-quadratic and polynomial mixtures /[electronic resource] :by Yannick Deville, Leonardo Tomazeli Duarte, Shahram Hosseini. - Cham :Springer International Publishing :2021. - ix, 71 p. :ill., digital ;24 cm. - SpringerBriefs in electrical and computer engineering,2191-8112. - SpringerBriefs in electrical and computer engineering..

Introduction -- Expressions and variants of the linear-quadratic mixing model -- Invertibility of mixing model, separating structures -- Independent component analysis and Bayesian separation methods -- Matrix factorization methods -- Sparse component analysis methods -- Extensions and conclusion -- Bilinear Sparse Component Analysis methods based on single source zones -- Conclusion.

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. Presents advanced configurations of the blind source separation problem, involving bilinear, linear-quadratic and polynomial mixing models; Provides a detailed and coherent description of the methods reported in the literature for handling these types of mixing phenomena; Focuses on complex configurations involving nonlinear mixing transforms.

ISBN: 9783030649777$q(electronic bk.)

Standard No.: 10.1007/978-3-030-64977-7doiSubjects--Topical Terms:

340034
Blind source separation.

LC Class. No.: TK5102.9

Dewey Class. No.: 621.3822

Nonlinear blind source separation and blind mixture identificationmethods for bilinear, linear-quadratic and polynomial mixtures /
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505 0 $a Introduction -- Expressions and variants of the linear-quadratic mixing model -- Invertibility of mixing model, separating structures -- Independent component analysis and Bayesian separation methods -- Matrix factorization methods -- Sparse component analysis methods -- Extensions and conclusion -- Bilinear Sparse Component Analysis methods based on single source zones -- Conclusion.
520 $a 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. Presents advanced configurations of the blind source separation problem, involving bilinear, linear-quadratic and polynomial mixing models; Provides a detailed and coherent description of the methods reported in the literature for handling these types of mixing phenomena; Focuses on complex configurations involving nonlinear mixing transforms.
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