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Comparison of Two Neural Networks Approaches to Boolean Matrix Factorization
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SYSNO ASEP 0328074 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Comparison of Two Neural Networks Approaches to Boolean Matrix Factorization Title Srovnání dvou neuronových přístupů k boolevským rozkladům matic Author(s) Polyakov, P.Y. (RU)
Frolov, A. A. (RU)
Húsek, Dušan (UIVT-O) RID, SAI, ORCIDSource Title Networked Digital Technologies. - Los Alamitos : IEEE Computer Society, 2009 / Snášel V. ; Pokorný J. ; Pichappan P. ; El-Qawasmeh E. - ISBN 978-1-4244-4614-8 Pages s. 316-321 Number of pages 6 s. Action NDT 2009. International Conference on Networked Digital Technologies /1./ Event date 29.07.2009-31.07.2009 VEvent location Ostrava Country CZ - Czech Republic Event type WRD Language eng - English Country US - United States Keywords data mining ; artificial inteligence ; neural networks ; multivariate statistics ; Boolean factor analysis ; Hopfield-like neural networks ; feed forward neural network Subject RIV BB - Applied Statistics, Operational Research R&D Projects GA205/09/1079 GA ČR - Czech Science Foundation (CSF) 1M0567 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) CEZ AV0Z10300504 - UIVT-O (2005-2011) UT WOS 000279656200052 EID SCOPUS 70450205917 DOI https://doi.org/10.1109/NDT.2009.5272136 Annotation 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 when processed 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. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2010
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