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Statements

Subject Item
dbr:KernelICA
dbo:wikiPageWikiLink
dbr:Kernel-independent_component_analysis
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dbr:Kernel-independent_component_analysis
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dbr:Independent_component_analysis
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Kernel-independent component analysis
rdfs:comment
In statistics, kernel-independent component analysis (kernel ICA) is an efficient algorithm for independent component analysis which estimates source components by optimizing a generalized variance contrast function, which is based on representations in a reproducing kernel Hilbert space. Those contrast functions use the notion of mutual information as a measure of statistical independence.
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dbc:Statistical_algorithms
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912168648
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dbr:Independence_(probability_theory) dbr:Whitening_transformation dbc:Statistical_algorithms dbr:Reproducing_kernel_Hilbert_space dbr:Independent_component_analysis
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In statistics, kernel-independent component analysis (kernel ICA) is an efficient algorithm for independent component analysis which estimates source components by optimizing a generalized variance contrast function, which is based on representations in a reproducing kernel Hilbert space. Those contrast functions use the notion of mutual information as a measure of statistical independence.
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