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. Due to the
multidisciplinary nature of the subject, the book has been written
so as to appeal to an audience from very different backgrounds.
Basic mathematical skills (e.g. on matrix algebra and foundations
of probability theory) are essential in order to understand the
algorithms, although the book is written in an introductory,
accessible style.

This book offers a general overview of the basics of Blind
Source Separation, important solutions and algorithms, and in-depth
coverage of applications in image feature extraction, remote
sensing image fusion, mixed-pixel decomposition of SAR images,
image object recognition fMRI medical image processing, geochemical
and geophysical data mining, mineral resources prediction and
geoanomalies information recognition. Firstly, the background and
theory basics of blind source separation are introduced, which
provides the foundation for the following work. Matrix operation,
foundations of probability theory and information theory basics are
included here. There follows the fundamental mathematical model and
fairly new but relatively established blind source separation
algorithms, such as Independent Component Analysis (ICA) and its
improved algorithms (Fast ICA, Maximum Likelihood ICA, Overcomplete
ICA, Kernel ICA, Flexible ICA, Non-negative ICA, Constrained ICA,
Optimised ICA). The last part of the book considers the very recent
algorithms in BSS e.g. Sparse Component Analysis (SCA) and
Non-negative Matrix Factorization (NMF). Meanwhile, in-depth cases
are presented for each algorithm in order to help the reader
understand the algorithm and its application field.

* A systematic exploration of both classic and contemporary
algorithms in blind source separation with practical case
studies

* Presents new improved algorithms aimed at different
applications, such as image feature extraction, remote sensing
image fusion, mixed-pixel decomposition of SAR images, image object
recognition, and MRI medical image processing

* With applications in geochemical and geophysical data mining,
mineral resources prediction and geoanomalies information
recognition

* Written by an expert team with accredited innovations in blind
source separation and its applications in natural science

* Accompanying website includes a software system providing codes
for most of the algorithms mentioned in the book, enhancing the
learning experience

Essential reading for postgraduate students and researchers
engaged in the area of signal processing, data mining, image
processing and recognition, information, geosciences, life
sciences.

Xianchuan Yu, Beijing Normal University, P. R. China

Dan Hu, Beijing Normal University, P. R. China

Jindong Xu, Beijing Normal University, P. R. China

Blind source separation is a relatively new signal processing method combining artificial neural networks, statistical information processing and information theory. It has tremendous potential in applications such as processing of speech, image, and biomedical signals. The technique excels in signal extraction, enhancement, denoising, model reduction and classification problems.

This book provides an overview of the basics of blind source separation along with important solutions and algorithms. Applications are also covered in-depth, including image feature extraction, remote sensing image fusion, mixed-pixel decomposition of SAR images, image object recognition, fMRI medical image processing, geochemical and geophysical data mining, mineral resources prediction and geo-anomalies information recognition. Given the multidisciplinary nature of the subject the book has been written in an accessible style so as to appeal to readers from very different backgrounds.

• Gives a systematic exploration of both classic and contemporary algorithms in blind source separation with practical case studies

• Written by an expert team with innovations in blind source separation and its applications in natural science

• Codes for most of the algorithms mentioned in the book available from the author

This book is aimed at graduate students and researchers engaged in the areas of
signal processing, data mining, image processing and recognition, computational
geosciences, computational life sciences, and other field sciences.

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. Due to the multidisciplinary nature of the subject, the book has been written so as to appeal to an audience from very different backgrounds. Basic mathematical skills (e.g. on matrix algebra and foundations of probability theory) are essential in order to understand the algorithms, although the book is written in an introductory, accessible style.

This book offers a general overview of the basics of Blind Source Separation, important solutions and algorithms, and in-depth coverage of applications in image feature extraction, remote sensing image fusion, mixed-pixel decomposition of SAR images, image object recognition fMRI medical image processing, geochemical and geophysical data mining, mineral resources prediction and geoanomalies information recognition. Firstly, the background and theory basics of blind source separation are introduced, which provides the foundation for the following work. Matrix operation, foundations of probability theory and information theory basics are included here. There follows the fundamental mathematical model and fairly new but relatively established blind source separation algorithms, such as Independent Component Analysis (ICA) and its improved algorithms (Fast ICA, Maximum Likelihood ICA, Overcomplete ICA, Kernel ICA, Flexible ICA, Non-negative ICA, Constrained ICA, Optimised ICA). The last part of the book considers the very recent algorithms in BSS e.g. Sparse Component Analysis (SCA) and Non-negative Matrix Factorization (NMF). Meanwhile, in-depth cases are presented for each algorithm in order to help the reader understand the algorithm and its application field.

• A systematic exploration of both classic and contemporary algorithms in blind source separation with practical case studies
• Presents new improved algorithms aimed at different applications, such as image feature extraction, remote sensing image fusion, mixed-pixel decomposition of SAR images, image object recognition, and MRI medical image processing
• With applications in geochemical and geophysical data mining, mineral resources prediction and geoanomalies information recognition
• Written by an expert team with accredited innovations in blind source separation and its applications in natural science
• Accompanying website includes a software system providing codes for most of the algorithms mentioned in the book, enhancing the learning experience

Essential reading for postgraduate students and researchers engaged in the area of signal processing, data mining, image processing and recognition, information, geosciences, life sciences.

About the Authors xiii

Preface xv

Acknowledgements xvii

Glossary xix

1 Introduction 1

1.1 Overview of Blind S…