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Description
  • Boolean factor analysis is one of the most efficient methods to reveal and to overcome informational redundancy of high-dimensional binary signals. In the present study, we introduce new Expectation-Maximization method which maximizes the likelihood of Boolean factor analysis solution. Using the so-called bars problem benchmark, we compare efficiencies of the proposed method with Dendritic Inhibition neural network.
  • Boolean factor analysis is one of the most efficient methods to reveal and to overcome informational redundancy of high-dimensional binary signals. In the present study, we introduce new Expectation-Maximization method which maximizes the likelihood of Boolean factor analysis solution. Using the so-called bars problem benchmark, we compare efficiencies of the proposed method with Dendritic Inhibition neural network. (en)
Title
  • Boolean Factor Analysis by Expectation-Maximization Method
  • Boolean Factor Analysis by Expectation-Maximization Method (en)
skos:prefLabel
  • Boolean Factor Analysis by Expectation-Maximization Method
  • Boolean Factor Analysis by Expectation-Maximization Method (en)
skos:notation
  • RIV/67985807:_____/13:00368469!RIV13-GA0-67985807
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  • P(GA205/09/1079), P(GAP202/10/0262), Z(AV0Z10300504)
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  • 63736
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  • RIV/67985807:_____/13:00368469
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  • neural networks; hidden pattern search; Boolean factor analysis; generative model; information redundancy; exceptation-maximization (en)
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  • [03CC5A50C91C]
http://linked.open...v/mistoKonaniAkce
  • Prague
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  • Heidelberg
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  • Proceedings of the Third International Conference on Intelligent Human Computer Interaction IHCI 2011
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  • Frolov, A. A.
  • Húsek, Dušan
  • Polyakov, P. Y.
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  • 000312116400021
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number of pages
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  • Springer-Verlag
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  • 978-3-642-31602-9
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