Advances in Independent Component Analysis
Springer Science & Business Media, Jul 17, 2000 - Computers - 284 pages
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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2OO 4OO 6OO 2OOO algorithm Attias Bayesian blind separation blind signal separation blind source separation Centre Helsinki University components Sj condition number Data Set define dependence estimate Everson factor analysis figure FIN-02015 HUT Finland Gaussian sources Generalised Autoregressive generalised exponentials hidden Markov models HMICA model ICA model Independent Component Analysis independent subspace analysis Initialisation innovations probability Kalman filter Laplace's approximation Laplacian learning rule linear log likelihood m-tuple maximising Maximum likelihood method minimisation mixing matrix model the sources Monte Carlo integration Networks Research Centre Neural Computation Neural Networks Research non-Gaussian non-stationary ICA normalising number of sources observation equation observation xt observational noise p(at p(at\Xt p(xt\At particle filters Principal component analysis probability density problem Research Centre Helsinki singular source densities source model static ICA stochastic swarm of particles T. J. Sejnowski Technology P.O. Box topographic ICA tracking the mixing true mixing matrix University of Paisley unmixing variance vector Xt-i