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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 growin...

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Bibliographic Details
Main Author: Girolami, Mark
Format: eBook
Language:English
Published: London Springer London 1999, 1999
Edition:1st ed. 1999
Series:Perspectives in Neural Computing
Subjects:
Computation By Abstract Devices
Pattern Recognition
Computers
Artificial Intelligence
Online Access:
Collection: Springer Book Archives -2004 - Collection details see MPG.ReNa
Table of Contents:
  • 8. Temporal Anti-Hebbian Learning
  • 8.1 Introduction
  • 8.2 Blind Source Separation of Convolutive Mixtures
  • 8.3 Temporal Linear Anti-Hebbian Model
  • 8.4 Comparative Simulation
  • 8.5 Review of Existing Work on Adaptive Separation of Convolutive Mixtures
  • 8.6 Maximum Likelihood Estimation and Source Separation
  • 8.7 Temporal Anti-Hebbian Learning Based on Maximum Likelihood Estimation
  • 8.8 Comparative Simulations Using Varying PDF Models
  • 8.9 Conclusions
  • 9. Applications
  • 9.1 Introduction
  • 9.2 Industrial Applications
  • 9.3 Biomedical Applications
  • 9.4 ICA: A Data Mining Tool
  • 9.5 Experimental Results
  • 9.6 Conclusions
  • References
  • 6. Non-Linear Feature Extraction and Blind Source Separation
  • 6.1 Introduction
  • 6.2 Structure Identification in Multivariate Data
  • 6.3 Neural Network Implementation of Exploratory Projection Pursuit
  • 6.4 Neural Exploratory Projection Pursuit and Blind Source Separation
  • 6.5 Kurtosis Extrema
  • 6.6 Finding Interesting and Independent Directions
  • 6.7 Finding Multiple Interesting and Independent Directions Using Symmetric Feedback and Adaptive Whitening
  • 6.8 Finding Multiple Interesting and Independent Directions Using Hierarchic Feedback and Adaptive Whitening
  • 6.9 Simulations
  • 6.10 Adaptive BSS Using a Deflationary EPP Network
  • 6.11 Conclusions
  • 7. Information Theoretic Non-Linear Feature Extraction And Blind Source Separation
  • 7.1 Introduction
  • 7.2 Information Theoretic Indices for EPP
  • 7.3 Maximum Negentropy Learning
  • 7.4 General Maximum Negentropy Learning
  • 7.5 Stability Analysis of Generalised Algorithm
  • 7.6 Simulation Results
  • 7.7 Conclusions
  • 1. Introduction
  • 1.1 Self-Organisation and Blind Signal Processing
  • 1.2 Outline of Book Chapters
  • 2. Background to Blind Source Separation
  • 2.1 Problem Formulation
  • 2.2 Entropy and Information
  • 2.3 A Contrast Function for ICA
  • 2.4 Cumulant Expansions of Probability Densities and Higher Order Statistics
  • 2.5 Gradient Based Function Optimisation
  • 3. Fourth Order Cumulant Based Blind Source Separation
  • 3.1 Early Algorithms and Techniques
  • 3.2 The Method of Contrast Minimisation
  • 3.3 Adaptive Source Separation Methods
  • 3.4 Conclusions
  • 4. Self-Organising Neural Networks
  • 4.1 Linear Self-Organising Neural Networks
  • 4.2 Non-Linear Self-Organising Neural Networks
  • 4.3 Conclusions
  • 5. The Non-Linear PCA Algorithm and Blind Source Separation
  • 5.1 Introduction
  • 5.2 Non-Linear PCA Algorithm and Source Separation
  • 5.3 Non-Linear PCA Algorithm Cost Function
  • 5.4 Non-Linear PCA Algorithm Activation Function
  • 5.5 Conclusions

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