Abstract
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.
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