Self-Organising Neural Networks

Independent Component Analysis and Blind Source Separation

Häftad, Engelska, 1999

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This volume presents the theory and applications of self organizing neural network models which perform the independent component analysis (ICA) transformation and blind source separation (BSS). It covers the fundamental concepts of information theory, higher order statistics and in formation 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 visualization, data mining and biomedical data analysis.

Produktinformation

Utgivningsdatum1999-06-25
Mått155 x 235 x 16 mm
Vikt435 g
FormatHäftad
SpråkEngelska
SeriePerspectives in Neural Computing
Antal sidor271
Upplaga99001
FörlagSpringer London Ltd
ISBN9781852330668

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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.- 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.- 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.