Ebook: Advances in Independent Component Analysis

Independent Component Analysis (ICA) is a fast developing area of intense research interest. Following on from Self-Organising Neural Networks: Independent Component Analysis and Blind Signal Separation, this book reviews the significant developments of the past year.
It covers topics such as the use of hidden Markov methods, the independence assumption, and topographic ICA, and includes tutorial chapters on Bayesian and variational approaches. It also provides the latest approaches to ICA problems, including an investigation into certain "hard problems" for the very first time.
Comprising contributions from the most respected and innovative researchers in the field, this volume will be of interest to students and researchers in computer science and electrical engineering; research and development personnel in disciplines such as statistical modelling and data analysis; bio-informatic workers; and physicists and chemists requiring novel data analysis methods.

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Independent Component Analysis (ICA) is a fast developing area of intense research interest. Following on from Self-Organising Neural Networks: Independent Component Analysis and Blind Signal Separation, this book reviews the significant developments of the past year.
It covers topics such as the use of hidden Markov methods, the independence assumption, and topographic ICA, and includes tutorial chapters on Bayesian and variational approaches. It also provides the latest approaches to ICA problems, including an investigation into certain "hard problems" for the very first time.
Comprising contributions from the most respected and innovative researchers in the field, this volume will be of interest to students and researchers in computer science and electrical engineering; research and development personnel in disciplines such as statistical modelling and data analysis; bio-informatic workers; and physicists and chemists requiring novel data analysis methods.

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Independent Component Analysis (ICA) is a fast developing area of intense research interest. Following on from Self-Organising Neural Networks: Independent Component Analysis and Blind Signal Separation, this book reviews the significant developments of the past year.
It covers topics such as the use of hidden Markov methods, the independence assumption, and topographic ICA, and includes tutorial chapters on Bayesian and variational approaches. It also provides the latest approaches to ICA problems, including an investigation into certain "hard problems" for the very first time.
Comprising contributions from the most respected and innovative researchers in the field, this volume will be of interest to students and researchers in computer science and electrical engineering; research and development personnel in disciplines such as statistical modelling and data analysis; bio-informatic workers; and physicists and chemists requiring novel data analysis methods.
Content:
Front Matter....Pages I-XIX
Front Matter....Pages 1-1
Hidden Markov Independent Component Analysis....Pages 3-22
Particle Filters for Non-Stationary ICA....Pages 23-41
Front Matter....Pages 43-43
The Independence Assumption: Analyzing the Independence of the Components by Topography....Pages 45-62
The Independence Assumption: Dependent Component Analysis....Pages 63-71
Front Matter....Pages 73-73
Ensemble Learning....Pages 75-92
Bayesian Non-Linear Independent Component Analysis by Multi-Layer Perceptrons....Pages 93-121
Ensemble Learning for Blind Image Separation and Deconvolution....Pages 123-141
Front Matter....Pages 143-143
Multi-Class Independent Component Analysis (MUCICA) for Rank-Deficient Distributions....Pages 145-160
Blind Separation of Noisy Image Mixtures....Pages 161-181
Searching for Independence in Electromagnetic Brain Waves....Pages 183-199
ICA on Noisy Data: A Factor Analysis Approach....Pages 201-215
Analysis of Optical Imaging Data Using Weak Models and ICA....Pages 217-233
Independent Components in Text....Pages 235-256
Seeking Independence Using Biologically-Inspired ANN’s....Pages 257-276
Back Matter....Pages 277-279

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Independent Component Analysis (ICA) is a fast developing area of intense research interest. Following on from Self-Organising Neural Networks: Independent Component Analysis and Blind Signal Separation, this book reviews the significant developments of the past year.
It covers topics such as the use of hidden Markov methods, the independence assumption, and topographic ICA, and includes tutorial chapters on Bayesian and variational approaches. It also provides the latest approaches to ICA problems, including an investigation into certain "hard problems" for the very first time.
Comprising contributions from the most respected and innovative researchers in the field, this volume will be of interest to students and researchers in computer science and electrical engineering; research and development personnel in disciplines such as statistical modelling and data analysis; bio-informatic workers; and physicists and chemists requiring novel data analysis methods.
Content:
Front Matter....Pages I-XIX
Front Matter....Pages 1-1
Hidden Markov Independent Component Analysis....Pages 3-22
Particle Filters for Non-Stationary ICA....Pages 23-41
Front Matter....Pages 43-43
The Independence Assumption: Analyzing the Independence of the Components by Topography....Pages 45-62
The Independence Assumption: Dependent Component Analysis....Pages 63-71
Front Matter....Pages 73-73
Ensemble Learning....Pages 75-92
Bayesian Non-Linear Independent Component Analysis by Multi-Layer Perceptrons....Pages 93-121
Ensemble Learning for Blind Image Separation and Deconvolution....Pages 123-141
Front Matter....Pages 143-143
Multi-Class Independent Component Analysis (MUCICA) for Rank-Deficient Distributions....Pages 145-160
Blind Separation of Noisy Image Mixtures....Pages 161-181
Searching for Independence in Electromagnetic Brain Waves....Pages 183-199
ICA on Noisy Data: A Factor Analysis Approach....Pages 201-215
Analysis of Optical Imaging Data Using Weak Models and ICA....Pages 217-233
Independent Components in Text....Pages 235-256
Seeking Independence Using Biologically-Inspired ANN’s....Pages 257-276
Back Matter....Pages 277-279
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