Girolami M. (ed.) Advances in Independent Component Analysis

Springer, 2000. — 286 p.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 modeling and data analysis; bio-informatic workers; and physicists and chemists requiring novel data analysis methods.Temporal ICA Models
Hidden Markov Independent Component Analysis
Particle Filters for Non-Stationary ICA
The Validity of the Independence Assumption
The Independence Assumption: Analyzing the Independence of the Components by Topography
The Independence Assumption: Dependent Component Analysis
Ensemble Learning and Applications
Ensemble Learning
Bayesian Non-Linear Independent Component Analysis by Multi-Layer Perceptrons
Ensemble Learning for Blind Image Separation and Deconvolution
Data Analysis and Applications
Multi-Class Independent Component Analysis (MUCICA) for Rank-Deficient Distributions
Blind Separation of Noisy Image Mixtures
Searching for Independence in Electromagnetic Brain Waves
ICA on Noisy Data: A Factor Analysis Approach
Analysis of Optical Imaging Data Using Weak Models and ICA
Independent Components in Text
Seeking Independence Using Biologically-Inspired ANN's