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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.

Índice: Incorporating Temporal Effects into ICA Models: Hidden Markov Independent Component Analysis. Particle Filters for Non-Stationary ICA.Considering the Validity of the Independence Assumption: The Independence Assumption: Analysing the Independence of the Components by Topography. The Independence Assumption: Dependent Component Analysis.Ensemble Learning and Applications to Nonlinear ICA and Image Processing: Ensemble Learning. Bayesian Nonlinear Independent Component Analysis by Multi-Layer Perceptrons. Ensemble Learning for Blind Image Separation and Deconvolution.Data Analysis and Applications: Multi-Cla... Etc.