Xianchuan Yu, Dan Hu, Jindong Xu

Blind Source Separation

• Gebundenes Buch

Produktbeschreibung

A systematic exploration of both classic and contemporaryalgorithms in blind source separation with practical casestudies

The book presents an overview of Blind Source Separation, arelatively new signal processing method. Due to themultidisciplinary nature of the subject, the book has been writtenso as to appeal to an audience from very different backgrounds.Basic mathematical skills (e.g. on matrix algebra and foundationsof probability theory) are essential in order to understand thealgorithms, although the book is written in an introductory,accessible style.

This book offers a general overview of the basics of BlindSource Separation, important solutions and algorithms, and in-depthcoverage of applications in image feature extraction, remotesensing image fusion, mixed-pixel decomposition of SAR images,image object recognition fMRI medical image processing, geochemicaland geophysical data mining, mineral resources prediction andgeoanomalies information recognition. Firstly, the background andtheory basics of blind source separation are introduced, whichprovides the foundation for the following work. Matrix operation,foundations of probability theory and information theory basics areincluded here. There follows the fundamental mathematical model andfairly new but relatively established blind source separationalgorithms, such as Independent Component Analysis (ICA) and itsimproved algorithms (Fast ICA, Maximum Likelihood ICA, OvercompleteICA, Kernel ICA, Flexible ICA, Non-negative ICA, Constrained ICA,Optimised ICA). The last part of the book considers the very recentalgorithms in BSS e.g. Sparse Component Analysis (SCA) andNon-negative Matrix Factorization (NMF). Meanwhile, in-depth casesare presented for each algorithm in order to help the readerunderstand the algorithm and its application field.

A systematic exploration of both classic and contemporaryalgorithms in blind source separation with practical casestudies
Presents new improved algorithms aimed at differentapplications, such as image feature extraction, remote sensingimage fusion, mixed-pixel decomposition of SAR images, image objectrecognition, and MRI medical image processing
With applications in geochemical and geophysical data mining,mineral resources prediction and geoanomalies informationrecognition
Written by an expert team with accredited innovations in blindsource separation and its applications in natural science
Accompanying website includes a software system providing codesfor most of the algorithms mentioned in the book, enhancing thelearning experience
Essential reading for postgraduate students and researchersengaged in the area of signal processing, data mining, imageprocessing and recognition, information, geosciences, lifesciences.

Produktdetails

• Verlag: Wiley & Sons
• 1. Auflage
• Seitenzahl: 416
• Erscheinungstermin: 28. Januar 2014
• Englisch
• Abmessung: 250mm x 175mm x 25mm
• Gewicht: 849g
• ISBN-13: 9781118679845
• ISBN-10: 1118679849
• Artikelnr.: 39560259

Autorenporträt

Xianchuan Yu, Beijing Normal University, P. R. China Dan Hu, Beijing Normal University, P. R. China Jindong Xu, Beijing Normal University, P. R. China

Inhaltsangabe

About the Authors xiii Preface xv Acknowledgements xvii Glossary xix 1
Introduction 1 1.1 Overview of Blind Source Separation 1 1.2 History of BSS
4 1.3 Applications of BSS 8 1.4 Contents of the Book 10 References 11 Part
I BASIC THEORY OF BSS 2 Mathematical Foundation of Blind Source Separation
19 2.1 Matrix Analysis and Computing 19 2.2 Foundation of Probability
Theory for Higher-Order Statistics 28 2.3 Basic Concepts of Information
Theory 33 2.4 Distance Measure 37 2.5 Solvability of the Signal Blind
Source Separation Problem 40 Further Reading 41 3 General Model and
Classical Algorithm for BSS 43 3.1 Mathematical Model 43 3.2 BSS Algorithm
46 References 51 4 Evaluation Criteria for the BSS Algorithm 53 4.1
Evaluation Criteria for Objective Functions 53 4.2 Evaluation Criteria for
Correlations 57 4.3 Evaluation Criteria for Signal-to-Noise Ratio 57
References 58 Part II INDEPENDENT COMPONENT ANALYSIS AND APPLICATIONS 5
Independent Component Analysis 61 5.1 History of ICA 61 5.2 Principle of
ICA 65 5.3 Chapter Summary 82 References 83 6 Fast Independent Component
Analysis and Its Application 85 6.1 Overview 85 6.2 FastICA Algorithm 89
6.3 Application and Analysis 92 6.4 Conclusion 118 References 119 7 Maximum
Likelihood Independent Component Analysis and Its Application 121 7.1
Overview 121 7.2 Algorithms for Maximum Likelihood Estimation 123 7.3
Application and Analysis 130 7.4 Chapter Summary 133 References 133 8
Overcomplete Independent Component Analysis Algorithms and Applications 135
8.1 Overcomplete ICA Algorithms 135 8.2 Applications and Analysis 139 8.3
Chapter Summary 143 References 144 9 Kernel Independent Component Analysis
145 9.1 KICA Algorithm 145 9.2 Application and Analysis 147 9.3 Concluding
Remarks 149 References 152 10 Natural Gradient Flexible ICA Algorithm and
Its Application 153 10.1 Natural Gradient Flexible ICA Algorithm 153 10.2
Application and Analysis 156 10.3 Chapter Summary 166 References 166 11
Non-negative Independent Component Analysis and Its Application 167 11.1
Non-negative Independent Component Analysis 168 11.2 Application and
Analysis 169 11.3 Chapter Summary 182 References 182 12 Constraint
Independent Component Analysis Algorithms and Applications 183 12.1
Overview 183 12.2 CICA Algorithm 185 12.3 Application and Analysis 189 12.4
Chapter Summary 196 References 196 13 Optimized Independent Component
Analysis Algorithms and Applications 199 13.1 Overview 199 13.2 Optimized
ICA Algorithm 200 13.3 Application and Analysis 205 13.4 Chapter Summary
221 References 222 14 Supervised Learning Independent Component Analysis
Algorithms and Applications 225 14.1 Overview 225 14.2 Mathematical Model
226 14.3 Principles of SL-ICA 227 14.4 SL-ICA Implementation Process 230
14.5 The Experiment 230 14.6 Chapter Summary 239 Appendix 14.A Polarization
Channel SAR Images of Beijing and the Decomposition Results using SL-ICA
239 References 242 Part III ADVANCES AND APPLICATIONS OF BSS 15
Non-negative Matrix Factorization Algorithms and Applications 247 15.1
Introduction 247 15.2 NMF Algorithms 251 15.3 Applications and Analysis 276
15.4 Chapter Summary 309 References 310 16 Sparse Component Analysis and
Applications 313 16.1 Overview 314 16.2 Linear Clustering SCA (LC-SCA) 321
16.3 Plane Clustering SCA (PC-SCA) 332 16.4 Over-Complete SCA Based on
Plane Clustering (PCO-SCA) 336 16.5 Blind Image Separation Based on
Wavelets and SCA (WL-SCA) 340 16.6 New BSS Algorithm Based on Feedback SCA
343 16.7 Remote Sensing Image Classification Based on SCA 351 16.8 Chapter
Summary 357 References 357 Index 361