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Attractor Neural Network Combined with Likelihood Maximization Algorithm for Boolean Factor Analysis
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SYSNO ASEP 0378749 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Attractor Neural Network Combined with Likelihood Maximization Algorithm for Boolean Factor Analysis Author(s) Frolov, A. (RU)
Húsek, Dušan (UIVT-O) RID, SAI, ORCID
Polyakov, P.Y. (CZ)Source Title 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 Pages s. 1-10 Number of pages 10 s. Publication form Print - P Action ISNN 2012. International Symposium on Neural Networks /9./ Event date 11.07.2012-14.07.2012 VEvent location Shenyang Country CN - China Event type WRD Language eng - English Country DE - Germany Keywords 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 Subject RIV IN - Informatics, Computer Science R&D Projects GAP202/10/0262 GA ČR - Czech Science Foundation (CSF) CEZ AV0Z10300504 - UIVT-O (2005-2011) EID SCOPUS 84865143797 DOI 10.1007/978-3-642-31346-2_1 Annotation 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. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2013
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