Self-Organising Neural Networks: Independent Component Analysis and Blind Source Separation
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.
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Background to Blind Source Separation
Fourth Order Cumulant Based Blind Source Separation
SelfOrganising Neural Networks
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activation function Amari anti-Hebbian learning approximate asymptotic stability Bell and Sejnowski blind source separation Cardoso Chapter Cichocki computed considered convergence cost function covariance matrix decorrelation dimensional eigenvalue entropy feedforward weights Figure filter fourth order cumulants Fyfe Hebbian learning Herrault higher order higher order statistics hyperbolic tangent images Independent Component Analysis indicates Infomax Infomax algorithm information theoretic input Iterations x1000 Jutten Karhunen and Joutsensalo kurtosis maximisation minimisation mixing mixtures of super-Gaussian multivariate mutual information natural gradient natural speech negative negentropy network output neuron noise non-linear PCA algorithm normalised normally distributed order statistics orthogonal orthonormal output neuron parameter performance index principal component probability density Projection Pursuit proposed second order Section self-organising neural networks separating solution separation of mixtures signal processing simulation source signals spatial whitening stationary points sub-Gaussian sources super-Gaussian sources symmetric tanh temporal transformation utilised variable variance weight matrix weight update Weights Development zero zero-mean