Blind Source Separation | Zookal Textbooks | Zookal Textbooks
  • Author(s) Xianchuan Yu / Dan Hu / Jindong Xu
  • SubtitleTheory and Applications
  • Edition1
  • Published7th January 2014
  • PublisherJohn Wiley & Sons (Asia)
  • ISBN9781118679845

Theory and Applications

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.

Blind Source Separation

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  • Author(s) Xianchuan Yu / Dan Hu / Jindong Xu
  • SubtitleTheory and Applications
  • Edition1
  • Published7th January 2014
  • PublisherJohn Wiley & Sons (Asia)
  • ISBN9781118679845

Theory and Applications

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.

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