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A systematic exploration of both classic and contemporary algorithms in blind source separation with practical case studies The book presents an overview of Blind Source Separation, a relatively new signal processing method.
List of contents
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
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About the author
Xianchuan Yu, Beijing Normal University, P. R. China
Dan Hu, Beijing Normal University, P. R. China
Jindong Xu, Beijing Normal University, P. R. China
Summary
A systematic exploration of both classic and contemporary algorithms in blind source separation with practical case studies The book presents an overview of Blind Source Separation, a relatively new signal processing method.
