Attractor Neural Network Combined with Likelihood Maximization Algorithm for Boolean Factor Analysis

Kód výsledku v IS VaVaI

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Nalezeny alternativní kódy

RIV/61989100:27740/12:86085643

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Attractor Neural Network Combined with Likelihood Maximization Algorithm for Boolean Factor Analysis

Popis výsledku v původním jazyce

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

Název v anglickém jazyce

Attractor Neural Network Combined with Likelihood Maximization Algorithm for Boolean Factor Analysis

Popis výsledku anglicky

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

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

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

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2012

Kód důvěrnosti údajů

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

Název statě ve sborníku

Advances in Neural Networks - ISNN 2012

ISBN

978-3-642-31345-5

ISSN

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e-ISSN

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Počet stran výsledku

10

Strana od-do

1-10

Název nakladatele

Springer

Místo vydání

Berlin

Místo konání akce

Shenyang

Datum konání akce

11. 7. 2012

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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