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

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.

---

This volume presents the theory and applications of self-organising neural network models which perform the Independent Component Analysis (ICA) transformation and Blind Source Separation (BSS). It is largely self-contained, covering the fundamental concepts of information theory, higher order statistics and information geometry. Neural models for instantaneous and temporal BSS and their adaptation algorithms are presented and studied in detail. There is also in-depth coverage of the following application areas; noise reduction, speech enhancement in noisy environments, image enhancement, feature extraction for classification, data analysis and visualisation, data mining and biomedical data analysis. Self-Organising Neural Networks will be of interest to postgraduate students and researchers in Connectionist AI, Signal Processing and Neural Networks, research and development workers, and technology development engineers and research engineers.

---

This volume presents the theory and applications of self-organising neural network models which perform the Independent Component Analysis (ICA) transformation and Blind Source Separation (BSS). It is largely self-contained, covering the fundamental concepts of information theory, higher order statistics and information geometry. Neural models for instantaneous and temporal BSS and their adaptation algorithms are presented and studied in detail. There is also in-depth coverage of the following application areas; noise reduction, speech enhancement in noisy environments, image enhancement, feature extraction for classification, data analysis and visualisation, data mining and biomedical data analysis. Self-Organising Neural Networks will be of interest to postgraduate students and researchers in Connectionist AI, Signal Processing and Neural Networks, research and development workers, and technology development engineers and research engineers.
Content:
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

---

This volume presents the theory and applications of self-organising neural network models which perform the Independent Component Analysis (ICA) transformation and Blind Source Separation (BSS). It is largely self-contained, covering the fundamental concepts of information theory, higher order statistics and information geometry. Neural models for instantaneous and temporal BSS and their adaptation algorithms are presented and studied in detail. There is also in-depth coverage of the following application areas; noise reduction, speech enhancement in noisy environments, image enhancement, feature extraction for classification, data analysis and visualisation, data mining and biomedical data analysis. Self-Organising Neural Networks will be of interest to postgraduate students and researchers in Connectionist AI, Signal Processing and Neural Networks, research and development workers, and technology development engineers and research engineers.
Content:
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
....