About: Two Expectation-Maximization Algorithms for Boolean Factor Analysis

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An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

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rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
Description	Methods for the discovery of hidden structures of high-dimensional binary data are one of the most important challenges facing the community of machine learning researchers. There are many approaches in the literature that try to solve this hitherto rather ill-defined task. In the present, we propose a general generative model of binary data for Boolean Factor Analysis and introduce two new Expectation-Maximization Boolean Factor Analysis algorithms which maximize the likelihood of a Boolean Factor Analysis solution. To show the maturity of our solutions we propose an informational measure of Boolean Factor Analysis efficiency. Using the so-called bars problem benchmark, we compare the efficiencies of the proposed algorithms to that of Dendritic Inhibition Neural Network, Maximal Causes Analysis, and Boolean Matrix Factorization. Last mentioned methods were taken as related methods as they are supposed to be the most efficient in bars problem benchmark. Then we discuss the peculiarities of the two methods we proposed and the three related methods in performing Boolean Factor Analysis. Methods for the discovery of hidden structures of high-dimensional binary data are one of the most important challenges facing the community of machine learning researchers. There are many approaches in the literature that try to solve this hitherto rather ill-defined task. In the present, we propose a general generative model of binary data for Boolean Factor Analysis and introduce two new Expectation-Maximization Boolean Factor Analysis algorithms which maximize the likelihood of a Boolean Factor Analysis solution. To show the maturity of our solutions we propose an informational measure of Boolean Factor Analysis efficiency. Using the so-called bars problem benchmark, we compare the efficiencies of the proposed algorithms to that of Dendritic Inhibition Neural Network, Maximal Causes Analysis, and Boolean Matrix Factorization. Last mentioned methods were taken as related methods as they are supposed to be the most efficient in bars problem benchmark. Then we discuss the peculiarities of the two methods we proposed and the three related methods in performing Boolean Factor Analysis. (en)
Title	Two Expectation-Maximization Algorithms for Boolean Factor Analysis Two Expectation-Maximization Algorithms for Boolean Factor Analysis (en)
skos:prefLabel	Two Expectation-Maximization Algorithms for Boolean Factor Analysis Two Expectation-Maximization Algorithms for Boolean Factor Analysis (en)
skos:notation	RIV/67985807:_____/14:00369641!RIV15-GA0-67985807
http://linked.open...avai/riv/aktivita	P Z
http://linked.open...avai/riv/aktivity	P(ED1.1.00/02.0070), P(EE.2.3.20.0073), P(GAP202/10/0262), Z(AV0Z10300504)
http://linked.open...iv/cisloPeriodika	23 April
http://linked.open...vai/riv/dodaniDat	2015
http://linked.open...aciTvurceVysledku	Húsek, Dušan
http://linked.open.../riv/druhVysledku	J - Článek v odborném periodiku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	Ústav informatiky AV ČR, v. v. i.
http://linked.open...dnocenehoVysledku	51496
http://linked.open...ai/riv/idVysledku	RIV/67985807:_____/14:00369641
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	Boolean Factor analysis; Binary Matrix factorization; Neural networks; Binary data model; Dimension reduction; Bars problem (en)
http://linked.open.../riv/klicoveSlovo	Dimension reduction Bars problem Binary Matrix factorization Neural networks Binary data model Boolean Factor analysis
http://linked.open...odStatuVydavatele	NL - Nizozemsko
http://linked.open...ontrolniKodProRIV	[417ADCDF2DDA]
http://linked.open...i/riv/nazevZdroje	Neurocomputing
http://linked.open...in/vavai/riv/obor	IN
http://linked.open...ichTvurcuVysledku	1 (xsd:int)
http://linked.open...cetTvurcuVysledku	3 (xsd:int)
http://linked.open...vavai/riv/projekt	Bio-Inspired Methods: research, development and knowledge transfer IT4Innovations Centre of Excellence Decompositions of matrices with binary and ordinal data: theory, algorithms, and complexity
http://linked.open...UplatneniVysledku	2014
http://linked.open...v/svazekPeriodika	130
http://linked.open...iv/tvurceVysledku	Frolov, A. A. Húsek, Dušan Polyakov, P. Y.
http://linked.open...ain/vavai/riv/wos	000333233200012
http://linked.open...n/vavai/riv/zamer	Informatika pro informační společnost: modely, algoritmy, aplikace
issn	0925-2312
number of pages	15 (xsd:int)
http://bibframe.org/vocab/doi	10.1016/j.neucom.2012.02.055

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