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 sector of extreme examine curiosity. Following on from Self-Organising Neural Networks: self sustaining part research and Blind sign Separation, this ebook stories the numerous advancements of the prior year.
It covers themes corresponding to using hidden Markov tools, the independence assumption, and topographic ICA, and comprises instructional chapters on Bayesian and variational methods. It additionally presents the most recent ways to ICA difficulties, together with an research into definite "hard difficulties" for the first actual time.
Comprising contributions from the main revered and leading edge researchers within the box, this quantity might be of curiosity to scholars and researchers in desktop technology and electric engineering; learn and improvement team of workers in disciplines equivalent to statistical modelling and knowledge research; bio-informatic staff; and physicists and chemists requiring novel facts research methods.
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Additional info for Advances in Independent Component Analysis
D. J. C. MacKay. Maximum likelihood and covariant algorithms for independent component analysis. Technical report, University of Cambridge, December 1996. uk/mackay/. 18. J. J. K. 0' Ruanaidth and W. J. Fitzgerald. Numerical Bayesian Methods Applied to Signal Processing. Springer, 1996. 2 Non-Stationary ICA 41 19. A. Papoulis. Probability, Random Variables and Stochastic Processes. McGraw-Hill, 1991. 20. B. Pearlmutter and L. Parra. A context-sensitive generalization of ICA. In International Conference on Neural Information Processing, 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. 14. W. D. Penny and S. J. Roberts. Dynamic models for nonstationaxy signal segmentation. Computers and Biomedical Research, 1999. To appear. 15. W. H. Press, S. A. Teukolsky, W. T. Vetterling, and B.
40 Everson and Roberts References 1. S. Amari, A. Cichocki, and H. Yang. A new learning algorithm for blind signal separation. In D. Touretzky, M. Mozer, and M. Hasselmo, editors, Advances in Neural Information Processing Systems, 8, 757-763, MIT Press, Cambridge MA,1996. 2. H. Attias. Independent factor analysis. Neural Computation, 11(5):803-852, 1999. 3. A. J. Bell and T. J. Sejnowski. An information-maximization approach to blind separation and blind deconvolution. Neural Computation, 7(6):11291159, 1995.