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An Information-Maximization Approach to Blind Separation and Blind Deconvolution

Author

Terrence J. Sejnowski

Published in: Neural Computation

Date of publication: November 1995

Language: English

Main subject: independent component analysis, deconvolution, artificial neural network

Bibliographic databases:

Cites articles 12

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First- and Second-Order Methods for Learning: Between Steepest Descent and Newton's Method
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Self-organizing neural network that discovers surfaces in random-dot stereograms
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Local Synaptic Learning Rules Suffice to Maximize Mutual Information in a Linear Network
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Learning Factorial Codes by Predictability Minimization
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Learning by maximizing the information transfer through nonlinear noisy neurons and "noise breakdown"
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Independent component analysis, A new concept?
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Representation and separation of signals using nonlinear PCA type learning
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Separation of a mixture of independent signals using time delayed correlations

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