Two Expectation-Maximization algorithms for Boolean Factor Analysis
Two Expectation-Maximization algorithms for Boolean Factor Analysis
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DOI：
10.1016/j.neucom.2012.02.055
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发表时间：
2014-04-23
期刊：
影响因子：
6
通讯作者：
Polyakov, Pavel Y.
中科院分区：
文献类型：
--
作者：
Frolov, Alexander A.;Husek, Dusan;Polyakov, Pavel Y.
发现高维二进制数据隐藏结构的方法是机器学习研究人员面临的最重要的挑战之一。文献中有许多方法试图解决这一迄今定义相当模糊的任务。在本研究中，我们提出了一个用于布尔因子分析的二进制数据的一般生成模型，并引入了两个新的期望最大化布尔因子分析算法，它们最大化了布尔因子分析解的可能性。为了显示我们的解决方案的成熟度，我们提出了一个布尔因子分析效率的信息度量。使用所谓的BARS问题基准，我们将所提出的算法与树状抑制神经网络、最大原因分析和布尔矩阵分解的效率进行了比较。最后提到的方法被视为相关方法，因为它们被认为是BARS问题基准中最有效的方法。然后讨论了我们提出的两种方法的特点以及进行布尔因子分析的三种相关方法。(C)2013爱思唯尔B.V.保留所有权利。
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.