Two Expectation-Maximization Algorithms for Boolean Factor Analysis

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F14%3A86093200" target="_blank" >RIV/61989100:27240/14:86093200 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/67985807:_____/14:00369641 RIV/61989100:27740/14:86093200

Výsledek na webu

<a href="http://www.sciencedirect.com/science/article/pii/S0925231213006954" target="_blank" >http://www.sciencedirect.com/science/article/pii/S0925231213006954</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.neucom.2012.02.055" target="_blank" >10.1016/j.neucom.2012.02.055</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Two Expectation-Maximization Algorithms for Boolean Factor Analysis

Popis výsledku v původním jazyce

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 peculiaritie

Název v anglickém jazyce

Two Expectation-Maximization Algorithms for Boolean Factor Analysis

Popis výsledku anglicky

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 peculiaritie

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2014

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Neurocomputing

ISSN

0925-2312

e-ISSN

—

Svazek periodika

130

Číslo periodika v rámci svazku

23 April

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

15

Strana od-do

83-97

Kód UT WoS článku

000333233200012

EID výsledku v databázi Scopus

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