國立虎尾科技大學 |

Nonlinear Blind Source Separation and Blind Mixture Identification = Methods for Bilinear, Linear-quadratic and Polynomial Mixtures /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Nonlinear Blind Source Separation and Blind Mixture Identification/ by Yannick Deville, Leonardo Tomazeli Duarte, Shahram Hosseini.
其他題名:	Methods for Bilinear, Linear-quadratic and Polynomial Mixtures /
作者:	Deville, Yannick.
其他作者:	Hosseini, Shahram.
面頁冊數:	IX, 71 p. 7 illus.online resource. :
Contained By:	Springer Nature eBook
標題:	Computational Mathematics and Numerical Analysis. -
電子資源:	https://doi.org/10.1007/978-3-030-64977-7
ISBN:	9783030649777

Nonlinear Blind Source Separation and Blind Mixture Identification = Methods 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. - 1st ed. 2021. - IX, 71 p. 7 illus.online resource. - SpringerBriefs in Electrical and Computer Engineering,2191-8120. - 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

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

669338
Computational Mathematics and Numerical Analysis.

LC Class. No.: TK5102.9

Dewey Class. No.: 621.382

Nonlinear Blind Source Separation and Blind Mixture Identification = Methods for Bilinear, Linear-quadratic and Polynomial Mixtures /
LDR:03457nam a22004215i 4500 001 1056559
003 DE-He213
005 20211025141140.0
007 cr nn 008mamaa
008 220103s2021 sz | s |||| 0|eng d
020 $a 9783030649777 $9 978-3-030-64977-7
024 7 $a 10.1007/978-3-030-64977-7 $2 doi
035 $a 978-3-030-64977-7
050 4 $a TK5102.9
050 4 $a TA1637-1638
072 7 $a TTBM $2 bicssc
072 7 $a TEC008000 $2 bisacsh
072 7 $a TTBM $2 thema
072 7 $a UYS $2 thema
082 0 4 $a 621.382 $2 23
100 1 $a Deville, Yannick. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1205943
245 1 0 $a Nonlinear Blind Source Separation and Blind Mixture Identification $h [electronic resource] : $b Methods for Bilinear, Linear-quadratic and Polynomial Mixtures / $c by Yannick Deville, Leonardo Tomazeli Duarte, Shahram Hosseini.
250 $a 1st ed. 2021.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a IX, 71 p. 7 illus. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a SpringerBriefs in Electrical and Computer Engineering, $x 2191-8120
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.
650 2 4 $a Computational Mathematics and Numerical Analysis. $3 669338
650 2 4 $a Computational Intelligence. $3 768837
650 2 4 $a Computer Imaging, Vision, Pattern Recognition and Graphics. $3 671334
650 1 4 $a Signal, Image and Speech Processing. $3 670837
650 0 $a Computer mathematics. $3 1199796
650 0 $a Computational intelligence. $3 568984
650 0 $a Optical data processing. $3 639187
650 0 $a Speech processing systems. $3 564428
650 0 $a Image processing. $3 557495
650 0 $a Signal processing. $3 561459
700 1 $a Hosseini, Shahram. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1361902
700 1 $a Duarte, Leonardo Tomazeli. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1361901
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030649760
776 0 8 $i Printed edition: $z 9783030649784
830 0 $a SpringerBriefs in Electrical and Computer Engineering, $x 2191-8112 $3 1253713
856 4 0 $u https://doi.org/10.1007/978-3-030-64977-7
912 $a ZDB-2-ENG
912 $a ZDB-2-SXE
950 $a Engineering (SpringerNature-11647)
950 $a Engineering (R0) (SpringerNature-43712)