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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Nonlinear Blind Source Separation and Blind Mixture Identification: Methods for Bilinear, Linear-Quadratic and Polynomial Mixtures

Nonlinear Blind Source Separation and Blind Mixture Identification: Methods for Bilinear, Linear-Quadratic and Polynomial Mixtures

Deville, Yannick
Duarte, Leonardo Tomazeli
Hosseini, Shahram

Paperback

Series: Springerbriefs in Electrical and Computer Engineering

Technology & EngineeringGeneral Computers

ISBN10: 3030649768
ISBN13: 9783030649760
Publisher: Springer Nature
Published: Feb 3 2021
Pages: 71
Weight: 0.29
Height: 0.17 Width: 6.14 Depth: 9.21
Language: English
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.

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