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Simple Neuron Models for Independent Component Analysis
 Int. Journal of Neural Systems
, 1997
"... Recently, several neural algorithms have been introduced for Independent Component Analysis. Here we approach the problem from the point of view of a single neuron. First, simple Hebbianlike learning rules are introduced for estimating one of the independent components from sphered data. Some of th ..."
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Cited by 26 (3 self)
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Recently, several neural algorithms have been introduced for Independent Component Analysis. Here we approach the problem from the point of view of a single neuron. First, simple Hebbianlike learning rules are introduced for estimating one of the independent components from sphered data. Some of the learning rules can be used to estimate an independent component which has a negative kurtosis, and the others estimate a component of positive kurtosis. Next, a twounit system is introduced to estimate an independent component of any kurtosis. The results are then generalized to estimate independent components from nonsphered (raw) mixtures. To separate several independent components, a system of several neurons with linear negative feedback is used. The convergence of the learning rules is rigorously proven without any unnecessary hypotheses on the distributions of the independent components.
A Comparative Survey on Adaptive Neural Network Algorithms for Independent Component Analysis
"... Abstract. The paper is an overview of the most frequently used neural network algorithms for implementing Independent Component Analysis (ICA). The performance of six structurally different algorithms was ranked in blind separation of independent artificially generated signals using the stationary l ..."
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Cited by 3 (1 self)
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Abstract. The paper is an overview of the most frequently used neural network algorithms for implementing Independent Component Analysis (ICA). The performance of six structurally different algorithms was ranked in blind separation of independent artificially generated signals using the stationary linear ICA model. Ranking of the estimated components was also carried out and compared among different ICA approaches. All algorithms were run with different contrast functions, which were optimally selected on the basis of maximizing the sum of individual negentropies of the network outputs or minimizing their mutual information. Both subgaussian and supergaussian onedimensional time series were employed throughout the numerical simulations.
nonGaussian sources
, 1998
"... An algebraic principle for blind separation of white ..."
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