Two Expectation-Maximization algorithms for Boolean Factor Analysis

doi:10.1016/j.neucom.2012.02.055

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Two Expectation-Maximization algorithms for Boolean Factor Analysis

2014-04-01

PRE

Alexander Frolov

Dušan Húsek *

Pavel Polyakov

DOI：10.1016/j.neucom.2012.02.055

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Abstract

En 中文

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. (C) 2013 Elsevier B.V. All rights reserved.

Keywords:

Boolean Factor Analysis

Binary Matrix Factorization

Neural networks

Binary data model

Dimension reduction

Bars problem