Advances in Independent Component Analysis

Front Cover
Mark Girolami
Springer Science & Business Media, Jul 17, 2000 - Computers - 284 pages
0 Reviews
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.
 

What people are saying - Write a review

We haven't found any reviews in the usual places.

Contents

Hidden Markov Independent Component Analysis
3
References 141
21
Particle Filters for NonStationary
23
Analyzing
45
Copyright

Other editions - View all

Common terms and phrases

References to this book

Bibliographic information