Self-Organising Neural Networks: Independent Component Analysis and Blind Source Separation

✍ Scribed by Mark Girolami BSc (Hons), BA, MSc, PhD, CEng, MIMechE, MIEE (auth.)

• Publisher: Springer-Verlag London
• Year: 1999
• Tongue: English
• Leaves: 275
• Series: Perspectives in Neural Computing
• Edition: 1
• Category: Library

✦ Synopsis

The conception of fresh ideas and the development of new techniques for Blind Source Separation and Independent Component Analysis have been rapid in recent years. It is also encouraging, from the perspective of the many scientists involved in this fascinating area of research, to witness the growing list of successful applications of these methods to a diverse range of practical everyday problems. This growth has been due, in part, to the number of promising young and enthusiastic researchers who have committed their efforts to expanding the current body of knowledge within this field of research. The author of this book is among one of their number. I trust that the present book by Dr. Mark Girolami will provide a rapid and effective means of communicating some of these new ideas to a wide international audience and that in turn this will expand further the growth of knowledge. In my opinion this book makes an important contribution to the theory of Independent Component Analysis and Blind Source Separation. This opens a range of exciting methods, techniques and algorithms for applied researchers and practitioner engineers, especially from the perspective of artificial neural networks and information theory. It has been interesting to see how rapidly the scientific literature in this area has grown.

✦ Table of Contents

Front Matter....Pages i-ix
Introduction....Pages 1-4
Background to Blind Source Separation....Pages 5-34
Fourth Order Cumulant Based Blind Source Separation....Pages 35-45
Self-Organising Neural Networks....Pages 47-75
The Non-Linear PCA Algorithm and Blind Source Separation....Pages 77-118
Non-Linear Feature Extraction and Blind Source Separation....Pages 119-163
Information Theoretic Non-Linear Feature Extraction and Blind Source Separation....Pages 165-200
Temporal Anti-Hebbian Learning....Pages 201-237
Applications....Pages 239-254
Back Matter....Pages 255-271

✦ Subjects

Artificial Intelligence (incl. Robotics); Pattern Recognition; Computation by Abstract Devices