Expectation-Maximization Approach to Boolean Factor Analysis

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F11%3A00368431" target="_blank" >RIV/67985807:_____/11:00368431 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/IJCNN.2011.6033270" target="_blank" >http://dx.doi.org/10.1109/IJCNN.2011.6033270</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/IJCNN.2011.6033270" target="_blank" >10.1109/IJCNN.2011.6033270</a>

Result language
angličtina
Original language name
Expectation-Maximization Approach to Boolean Factor Analysis
Original language description
Methods for hidden structure of high-dimensional binary data discovery are one of the most important challenges facing machine learning community researchers. There are many approaches in literature that try to solve this hitherto rather ill-defined task. In the present study, we propose a most general generative model of binary data for Boolean factor analysis and introduce new Expectation-Maximization Boolean Factor Analysis algorithm which maximizes likelihood of Boolean Factor Analysis solution. Using the so-called bars problem benchmark, we compare efficiencies of Expectation-Maximization Boolean Factor Analysis algorithm with Dendritic Inhibition neural network. Then we discuss advantages and disadvantages of both approaches as regards results quality and methods efficiency.
Czech name
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Czech description
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Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
Z - Vyzkumny zamer (s odkazem do CEZ)

Publication year
2011
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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