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024 7 _a10.1007/978-3-030-64977-7
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100 1 _aDeville, Yannick.
_eauthor.
_0(orcid)0000-0002-8769-2446
_1https://orcid.org/0000-0002-8769-2446
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_951289
245 1 0 _aNonlinear Blind Source Separation and Blind Mixture Identification
_h[electronic resource] :
_bMethods for Bilinear, Linear-quadratic and Polynomial Mixtures /
_cby Yannick Deville, Leonardo Tomazeli Duarte, Shahram Hosseini.
250 _a1st ed. 2021.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2021.
300 _aIX, 71 p. 7 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
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490 1 _aSpringerBriefs in Electrical and Computer Engineering,
_x2191-8120
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 _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.
650 0 _aSignal processing.
_94052
650 0 _aImage processing—Digital techniques.
_931565
650 0 _aComputer vision.
_951290
650 0 _aComputational intelligence.
_97716
650 0 _aMathematics—Data processing.
_931594
650 1 4 _aDigital and Analog Signal Processing.
_936907
650 2 4 _aComputer Imaging, Vision, Pattern Recognition and Graphics.
_931569
650 2 4 _aComputational Intelligence.
_97716
650 2 4 _aComputational Mathematics and Numerical Analysis.
_931598
700 1 _aDuarte, Leonardo Tomazeli.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_951291
700 1 _aHosseini, Shahram.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_951292
710 2 _aSpringerLink (Online service)
_951293
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030649760
776 0 8 _iPrinted edition:
_z9783030649784
830 0 _aSpringerBriefs in Electrical and Computer Engineering,
_x2191-8120
_951294
856 4 0 _uhttps://doi.org/10.1007/978-3-030-64977-7
912 _aZDB-2-ENG
912 _aZDB-2-SXE
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