Advances In Independent Component Analysis by Mark GirolamiAdvances In Independent Component Analysis by Mark Girolami

Advances In Independent Component Analysis

EditorMark Girolami

Paperback | August 17, 2000

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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.
Title:Advances In Independent Component AnalysisFormat:PaperbackDimensions:304 pagesPublished:August 17, 2000Publisher:Springer LondonLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:1852332638

ISBN - 13:9781852332631

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Table of Contents

Part I - Incorporating Temporal Effects into ICA Models: 1. Hidden Markov Independent Component Analysis 2. Particle Filters for Non-Stationary ICA.- Part II - Considering the Validity of the Independence Assumption: 3. The Independence Assumption: Analysing the Independence of the Components by Topography 4. The Independence Assumption: Dependent Component Analysis.- Part III - Ensemble Learning and Applications to Nonlinear ICA and Image Processing: 5. Ensemble Learning 6. Bayesian Nonlinear Independent Component Analysis by Multi-Layer Perceptrons 7. Ensemble Learning for Blind Image Separation and Deconvolution.- Part 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 Artificial Neural Networks.