Advances in Independent Component Analysis

[Mark Girolami]

New Book Now Available: 

Title: Advances in Independent Component Analysis 
Editor : Mark Girolami
Publisher : Springer Verlag ISBN 1-85233-263-8

Companion Website : http://www.dcs.ex.ac.uk/ica

Since 1995 the neural computing research community has produced a large
number of publications dedicated to the Blind Separation of Sources (BSS) 
or Independent Component Analysis (ICA). This book is the outcome of a
workshop which was held after the 1999 International Conference on
Artificial Neural Networks (ICANN) in Edinburgh, Scotland.  Some of the
most active and productive neural computing researchers gathered to
present and share new ideas, models and experimental results and this
volume documents the individual presentations. 

Individual Chapter Titles

1. Hidden Markov Independent Component Analysis 
Authors : W. Penny, R.Everson, S. Roberts

2. Particle Filters for Non-Stationary ICA 
Authors : R. Everson, S.Roberts

3. Analysing the Independence of the Components by Topography 
Authors : A.Hyvarinen, P. Hoyer, M. Inki. 

4. Dependent Component Analysis 
Author : A Barros

5. Ensemble Learning 
Authors : H. Lappalainen, J. Misken

6. Bayesian Non-linear ICA by MLP's 
Authors: H. Lappalainen, A. Honkela. 

7. Ensemble Learning for Blind Separation and Deconvolution 
Authors : J.Misken, D. MacKay. 

8. MUCICA for Rank-Deficient Distributions 
Authors : F. Palmieri, A.Budillon. 

9. Blind Separation of Noisy image Mixtures 
Author : L.K. Hansen. 

10. Searching for independence in Electromagnetic Brain Waves 
Authors : R.Vigario, J. Sarela, E. Oja. 

11. ICA on Noisy Data : A Factor Analysis Approach 
Author : S Ikeda. 

12. Analysis of Optical Imaging Data Using Weak Models and ICA 
Authors :J. Porril, J. Stone, J. Berwick, J. Mayhew, P. Coffey. 

13. Independent Components in Text.  
Authors : T. Kolenda, L. Hansen, S.Sigurdsson. 

14. Seeking Independence Using Biologically-Inspired ANN's 
Authors : P.Lai, D. Charles, C. Fyfe.