articleTop 1% cited
High-Order Contrasts for Independent Component Analysis
Neural Computation · 1999 · Vol. 11(1) · pp. 157–192
J.-F. Cardoso✉(Télécom Paris)
Abstract
This article considers high-order measures of independence for the independent component analysis problem and discusses the class of Jacobi algorithms for their optimization. Several implementations are discussed. We compare the proposed approaches with gradient-based techniques from the algorithmic point of view and also on a set of biomedical data.
Blind Source Separation TechniquesSpectroscopy and Chemometric AnalysesEEG and Brain-Computer InterfacesIndependent component analysisComponent (thermodynamics)Class (philosophy)Computer scienceSet (abstract data type)Independence (probability theory)ImplementationMathematical optimizationPoint (geometry)Algorithm
MeSH terms
AlgorithmsModels, NeurologicalModels, Statistical
Citations
1,216
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28.34
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32
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References
Equivariant adaptive source separation
IEEE Transactions on Signal Processing · 1996 · 1,350 citations
An Information-Maximization Approach to Blind Separation and Blind Deconvolution
Neural Computation · 1995 · 9,144 citations
Blind signal separation: statistical principles
Proceedings of the IEEE · 1998 · 1,859 citations
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IEEE Transactions on Biomedical Engineering · 2000 · 801 citations
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