Comparison of Two Neural Networks Approaches to Boolean Matrix Factorization

Result code in IS VaVaI
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Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Comparison of Two Neural Networks Approaches to Boolean Matrix Factorization
Original language description
In this paper we compare two new neural networks methods, aimed at solving the problem of optimal binary matrix Boolean factorization or Boolean factor analysis. Neural network based Boolean factor analysis is a suitable method for a very large binary data sets mining including web. Two types of neural networks based Boolean factor analyzers are analyzed. One based on feed forward neural network and second based on Hopfield-like recurrent neural network. We show that both methods give good results whenprocessed data have a simple structure. But as the complexity of data structure grows, method based on feed forward neural network loses the ability to solve the Boolean factor analysis. In the method, based on the Hopfield like recurrent neural network,this effect is not observed.
Czech name
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Czech description
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Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
Z - Vyzkumny zamer (s odkazem do CEZ)

Publication year
2009
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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