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.

---

Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.

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