By William D. Penny, Richard M. Everson, Stephen J. Roberts (auth.), Mark Girolami BSc (Hons), BA, MSc, PhD, CEng, MIEE, MIMechE (eds.)
Independent part research (ICA) is a quick constructing quarter of excessive examine curiosity. Following on from Self-Organising Neural Networks: self sufficient part research and Blind sign Separation, this booklet reports the numerous advancements of the previous year.
It covers subject matters equivalent to using hidden Markov equipment, the independence assumption, and topographic ICA, and contains educational chapters on Bayesian and variational ways. It additionally offers the most recent ways to ICA difficulties, together with an research into yes "hard difficulties" for the first actual time.
Comprising contributions from the main revered and cutting edge researchers within the box, this quantity may be of curiosity to scholars and researchers in desktop technology and electric engineering; study and improvement body of workers in disciplines resembling statistical modelling and knowledge research; bio-informatic employees; and physicists and chemists requiring novel info research methods.
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Extra resources for Advances in Independent Component Analysis
S. Touretzky, M. C. Mozer, and M. E. Hasselmo, editors, Advances in Neural Information Processing Systems 8. MIT Press, Cambridge, MA,1996. 2. H. Attias. Independent factor analysis. Neural Computation, 11(4):803-851, 1999. 3. H. Attias. Inferring parameters and structure of latent variable models by variational Bayes. In Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, 1999. ae . ukrhagai/papers. html 4. A. J. Bell and T. J. Sejnowski. An information-maximization approach to blind separation and blind deconvolution.
22 Penny et al 11. D. J. C. MacKay. Maximum likelihood and covaxiant algorithms for independent component analysis. Technical report, Cavendish Laboratory, University of Cambridge, 1996. 12. A. Papoulis. Probability, Random Variables, and Stochastic Processes. McGraw-Hill, 1991. 13. B. A. Pearlmutter and L. C. Parra. Maximum likelihood blind source separation: A context-sensitive generalization of ICA. In Advances in Neural Information Processing Systems 9, 613-619. MIT Press, Cambridge, MA, 1997.
Random sampling from the exponential power distribution. Journal of the American Statistical Association, 75:683-686, 1980. 20. M. E. Tipping and C. M. Bishop. Mixtures of probabilistic principal component analyzers. Neural Computation, 11(2):443-482, 1999. Part II The Validity of the Independence Assumption 2 Particle Filters for Non-Stationary leA Richard M. Everson and Stephen J. 1 Introduction Over the last decade in particular there has been much interest in Independent Component Analysis (ICA) methods for blind source separation (BSS) and deconvolution (see  for a review).