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020			_a9783030649777
024	7		_a10.1007/978-3-030-64977-7 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _erda _dES-MaUEC
050		4	_aTK5102.9 _b2021 EB
100	1		_aDeville, Yannick _eautor _0(orcid)0000-0002-8769-2446 _1https://orcid.org/0000-0002-8769-2446 _4aut _4http://id.loc.gov/vocabulary/relators/aut _9678369 _d1964-
245	1	0	_aNonlinear Blind Source Separation and Blind Mixture Identification : _bMethods for Bilinear, Linear-quadratic and Polynomial Mixtures _cby Yannick Deville, Leonardo Tomazeli Duarte, Shahram Hosseini
250			_aFirst edition 2021
264		1	_aCham _bSpringer International Publishing _c2021
300			_a1 recurso en línea (IX, 71 páginas) _b7 ilustraciones
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
490	0		_aSpringerBriefs in Electrical and Computer Engineering _x2191-8112
490	0		_aEngineering (SpringerNature-11647)
490	0		_aEngineering (R0) (SpringerNature-43712)
505	0		_aIntroduction -- 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	3		_aThis 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.
988			_aSpringer_Engineering_2021
650		7	_2embne _9150608 _aProceso de señales
700	1		_aDuarte, Leonardo Tomazeli _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9678370
700	1		_aHosseini, Shahram _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9678371
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-64977-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE _n0
998			_b04/2021 _dz _eb _zPRE