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Attractor Neural Network Combined with Likelihood Maximization Algorithm for Boolean Factor Analysis
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SYSNO ASEP 0378749 Druh ASEP C - Konferenční příspěvek (mezinárodní konf.) Zařazení RIV D - Článek ve sborníku Název Attractor Neural Network Combined with Likelihood Maximization Algorithm for Boolean Factor Analysis Tvůrce(i) Frolov, A. (RU)
Húsek, Dušan (UIVT-O) RID, SAI, ORCID
Polyakov, P.Y. (CZ)Zdroj.dok. Advances in Neural Networks - ISNN 2012, 1. - Berlin : Springer, 2012 / Wang J. ; Yen G.G. ; Polycarpou M.M. - ISSN 0302-9743 - ISBN 978-3-642-31345-5 Rozsah stran s. 1-10 Poč.str. 10 s. Forma vydání Tištěná - P Akce ISNN 2012. International Symposium on Neural Networks /9./ Datum konání 11.07.2012-14.07.2012 Místo konání Shenyang Země CN - Čína Typ akce WRD Jazyk dok. eng - angličtina Země vyd. DE - Německo Klíč. slova Associative Neural Network ; Likelihood Maximization ; Boolean Factor Analysis ; Binary Matrix factorization ; Noise XOR Mixing ; Plato Problem ; Information Gain ; Bars problem ; Data Mining ; Dimension Reduction ; Hebbian Learning ; Anti-Hebbian Learning Vědní obor RIV IN - Informatika CEP GAP202/10/0262 GA ČR - Grantová agentura ČR CEZ AV0Z10300504 - UIVT-O (2005-2011) EID SCOPUS 84865143797 DOI 10.1007/978-3-642-31346-2_1 Anotace When large data sets are analyzed, the pursuit of their appropriate representation in the space of lower dimension is a common practice. Boolean factor analysis can serve as a powerful tool to solve the task, when dealing with binary data. Here we provide a short insight into a new approach to Boolean factor analysis we have developed as an extension of our previously proposed method: Hopfield-like Attractor Neural Network with Increasing Activity. We have greatly enhanced its functionality, having complemented this method by maximizing the data set likelihood function. We have defined this Likelihood function on the basis of the data generative model proposed previously. As a result, in such a way we can obtain a full set of generative model parameters. We demonstrate the efficiency of the new method using the artificial signals, which are random mixtures of horizontal and vertical bars that are a benchmark for Boolean factor analysis. Then we show that the method can be used for real task solving when analyzing data from the Kyoto Encyclopedia of Genes and Genomes. Pracoviště Ústav informatiky Kontakt Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Rok sběru 2013
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