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Boolean Factor Analysis by Expectation-Maximization Method
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SYSNO ASEP 0368469 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Boolean Factor Analysis by Expectation-Maximization Method Author(s) Frolov, A. A. (RU)
Húsek, Dušan (UIVT-O) RID, SAI, ORCID
Polyakov, P.Y. (CZ)Source Title Proceedings of the Third International Conference on Intelligent Human Computer Interaction IHCI 2011. - Heidelberg : Springer, 2013 / Kudělka M. ; Pokorný J. ; Snášel V. ; Abraham A. - ISSN 2194-5357 - ISBN 978-3-642-31602-9 Pages s. 243-254 Number of pages 12 s. Publication form Print - P Action IHCI 2011. International Conference on Intelligent Human Computer Interaction /3./ Event date 29.08.2011-31.08.2011 VEvent location Prague Country CZ - Czech Republic Event type WRD Language eng - English Country DE - Germany Keywords neural networks ; hidden pattern search ; Boolean factor analysis ; generative model ; information redundancy ; exceptation-maximization Subject RIV IN - Informatics, Computer Science R&D Projects GAP202/10/0262 GA ČR - Czech Science Foundation (CSF) GA205/09/1079 GA ČR - Czech Science Foundation (CSF) CEZ AV0Z10300504 - UIVT-O (2005-2011) UT WOS 000312116400021 EID SCOPUS 84865620020 DOI 10.1007/978-3-642-31603-6_21 Annotation 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. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2013
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