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Boolean Factor Analysis by Attractor Neural Network
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SYSNO ASEP 0083501 Druh ASEP J - Článek v odborném periodiku Zařazení RIV J - Článek v odborném periodiku Poddruh J Článek ve WOS Název Boolean Factor Analysis by Attractor Neural Network Překlad názvu Boolevská faktorová analýza pomocí atraktorové neuronové sítě Tvůrce(i) Frolov, A. A. (RU)
Húsek, Dušan (UIVT-O) RID, SAI, ORCID
Muraviev, I. P. (RU)
Polyakov, P.Y. (RU)Zdroj.dok. IEEE Transactions on Neural Networks - ISSN 1045-9227
Roč. 18, č. 3 (2007), s. 698-707Poč.str. 10 s. Jazyk dok. eng - angličtina Země vyd. US - Spojené státy americké Klíč. slova recurrent neural network ; Hopfield-like neural network ; associative memory ; unsupervised learning ; neural network architecture ; neural network application ; statistics ; Boolean factor analysis ; dimensionality reduction ; features clustering ; concepts search ; information retrieval Vědní obor RIV BB - Aplikovaná statistika, operační výzkum CEP 1ET100300419 GA AV ČR - Akademie věd GA201/05/0079 GA ČR - Grantová agentura ČR CEZ AV0Z10300504 - UIVT-O (2005-2011) UT WOS 000246423400007 EID SCOPUS 34248662149 DOI 10.1109/TNN.2007.891664 Anotace A common problem encountered in disciplines such as statistics, data analysis, signal processing, textual data representation, and neural network research, is finding a suitable representation of the data in the lower dimension space. One of the principles used for this reason is a factor analysis. In this paper, we show that Hebbian learning and a Hopfield-like neural network could be used for a natural procedure for Boolean factor analysis (BFA). To ensure efficient BFA, we propose our original modification not only of Hopfield network architecture but also its dynamics as well. In this paper, we describe neural network implementation of the BFA method. We show the advantages of our Hopfield-like network modification step by step on artificially generated data. At the end, we show the efficiency of the method on artificial data containing a known list of factors. Our approach has the advantage of being able to analyze very large data sets while preserving the nature of the data. Pracoviště Ústav informatiky Kontakt Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Rok sběru 2008
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