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Comparison of Seven Methods for Boolean Factor Analysis and Their Evaluation by Information Gain

机译:布尔因素分析的七种方法的比较及其信息增益评估

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摘要

An usual task in large data set analysis is searching for an appropriate data representation in a space of fewer dimensions. One of the most efficient methods to solve this task is factor analysis. In this paper, we compare seven methods for Boolean factor analysis (BFA) in solving the so-called bars problem (BP), which is a BFA benchmark. The performance of the methods is evaluated by means of information gain. Study of the results obtained in solving BP of different levels of complexity has allowed us to reveal strengths and weaknesses of these methods. It is shown that the Likelihood maximization Attractor Neural Network with Increasing Activity (LANNIA) is the most efficient BFA method in solving BP in many cases. Efficacy of the LANNIA method is also shown, when applied to the real data from the Kyoto Encyclopedia of Genes and Genomes database, which contains full genome sequencing for 1368 organisms, and to text data set R52 (from Reuters 21578) typically used for label categorization.
机译:大型数据集分析中的一项常见任务是在较小维度的空间中搜索适当的数据表示形式。解决此任务的最有效方法之一是因子分析。在本文中,我们比较了布尔因子分析(BFA)的7种方法来解决所谓的酒吧问题(BP),这是BFA的基准。该方法的性能通过信息增益来评估。对解决不同复杂程度的BP所获得的结果的研究使我们能够揭示这些方法的优缺点。结果表明,在许多情况下,活动增加的似然最大化吸引子神经网络(LANNIA)是最有效的BFA方法。当将其应用于《京都基因与基因组百科全书》数据库的真实数据(包含用于1368种生物的全基因组测序)以及通常用于标签分类的文本数据集R52(来自路透社21578)时,LANNIA方法的有效性也得到了展示。 。

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