Two Expectation-Maximization Algorithms for Boolean Factor Analysis
The result's identifiers
Result code in 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>
Alternative codes found
RIV/67985807:_____/14:00369641 RIV/61989100:27740/14:86093200
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Two Expectation-Maximization Algorithms for Boolean Factor Analysis
Original language 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 peculiaritie
Czech name
—
Czech description
—
Classification
Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
IN - Informatics
OECD FORD branch
—
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Neurocomputing
ISSN
0925-2312
e-ISSN
—
Volume of the periodical
130
Issue of the periodical within the volume
23 April
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
15
Pages from-to
83-97
UT code for WoS article
000333233200012
EID of the result in the Scopus database
—