Two Expectation-Maximization algorithms for Boolean Factor Analysis

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 study, we...

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Bibliographic Details
Published in:Neurocomputing (Amsterdam) Vol. 130; pp. 83 - 97
Main Authors: Frolov, Alexander A., Husek, Dusan, Polyakov, Pavel Y.
Format: Journal Article
Language:English
Published: Elsevier B.V 23.04.2014
Subjects:
Algorithms
Bars
Bars problem
Benchmarking
Binary data
Binary data model
Binary Matrix Factorization
Boolean algebra
Boolean Factor Analysis
Dimension reduction
Factor analysis
Mathematical models
Neural networks
Dimension reduction
Binary Matrix Factorization
Boolean Factor Analysis
Binary data model
Neural networks
Bars problem
ISSN:0925-2312, 1872-8286
Online Access:Get full text
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Summary: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 study, 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 peculiarities of the two methods we proposed and the three related methods in performing Boolean Factor Analysis.
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ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2012.02.055