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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.
List of contents
I Temporal ICA Models.- 1 Hidden Markov Independent Component Analysis.- 2 Particle Filters for Non-Stationary ICA.- II The Validity of the Independence Assumption.- 3 The Independence Assumption: Analyzing the Independence of the Components by Topography.- 4 The Independence Assumption: Dependent Component Analysis.- III Ensemble Learning and Applications.- 5 Ensemble Learning.- 6 Bayesian Non-Linear Independent Component Analysis by Multi-Layer Perceptrons.- 7 Ensemble Learning for Blind Image Separation and Deconvolution.- IV Data Analysis and Applications.- 8 Multi-Class Independent Component Analysis (MUCICA) for Rank-Deficient Distributions.- 9 Blind Separation of Noisy Image Mixtures.- 10 Searching for Independence in Electromagnetic Brain Waves.- 11 ICA on Noisy Data: A Factor Analysis Approach.- 12 Analysis of Optical Imaging Data Using Weak Models and ICA.- 13 Independent Components in Text.- 14 Seeking Independence Using Biologically-Inspired ANN's.
