Expectation-Maximization Approach to Boolean Factor Analysis

0368431 - ÚI 2012 RIV US eng C - Konferenční příspěvek (zahraniční konf.)
Frolov, A. A. - Húsek, Dušan - Polyakov, P.Y.
Expectation-Maximization Approach to Boolean Factor Analysis.
IJCNN 2011 Conference Proceedings. Piscataway: IEEE, 2011, s. 559-566. ISBN 978-1-4244-9636-5.
[IJCNN 2011. International Joint Conference on Neural Networks. San Jose (US), 31.07.2011-05.08.2011]
Grant CEP: GA ČR GAP202/10/0262; GA ČR GA205/09/1079; GA MŠMT(CZ) 1M0567
Výzkumný záměr: CEZ:AV0Z10300504
Klíčová slova: Boolean factor analysis * bars problem * dendritic inhibition * expectation-maximization * neural network application * statistics
Kód oboru RIV: IN - Informatika

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.
Trvalý link: http://hdl.handle.net/11104/0202775