Expectation-Maximization Approach to Boolean Factor Analysis

Authors: A. A. Frolov, Dušan Húsek, P.Y. Polyakov
Publication year: 2011
Publication type: Konferenční příspěvek (mezinárodní konf.)
Periodical: IJCNN 2011 Conference Proceedings
Projects: Decompositions of matrices with binary and ordinal data: theory, algorithms, and complexity
Methods of Artificial Inteligence in GIS
Centre for Applied Cybernetics
Bibliographic citation: FROLOV, A. A., HÚSEK, Dušan, POLYAKOV, P.Y. Expectation-Maximization Approach to Boolean Factor Analysis. In: IJCNN 2011 Conference Proceedings. Piscataway: IEEE, 2011, S. 559-566. ISBN 978-1-4244-9636-5
Dostupné z: doi:10.1109/IJCNN.2011.6033270
DOI: 10.1109/IJCNN.2011.6033270
ID: 368431

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.