Gradual Information Maximization in Information Enhancement to Extract Important Input Neurons
Gradual Information Maximization in Information Enhancement to Extract Important Input Neurons
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DOI：
10.2316/p.2014.816-008
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
R. Kamimura;Ryozo Kitajima
中科院分区：
文献类型：
--
作者：
R. Kamimura;Ryozo Kitajima
本文提出了一种新的信息论方法“逐步信息最大化”来检测自组织映射中的重要输入神经元(变量)。信息增强方法被用来检测神经网络中的重要成分。然而，在信息增强方法中，我们发现用于检测重要神经元的信息并不一定是获取的。循序渐进的信息最大化旨在尽可能多地获取学习过程中产生的信息。这意味着，在学习的每个阶段积累的信息都可以用于检测重要神经元。我们将该方法应用于对东京都的一项民意调查。该方法明确提取了一个重要的变量--“会址”。通过仔细查阅该市的公开文件，我们发现，该市的“会场”问题被认为是最严重的财政问题之一。因此，渐进式信息最大化的发现是城市面临的一个重要问题。
In this paper, we propose a new type of informationtheoretic method called “gradual information maximization” to detect important input neurons (variables) in the self-organizing maps. The information enhancement method has been developed to detect important components in neural networks. However, in the information enhancement method, we have found that information for detecting important neurons is not necessarily acquired. The gradual information maximization aims to acquire information generated in the course of learning as much as possible. This means that information accumulated in every stage of learning can be used for the detection of important neurons. We applied the method to the analysis of a public poll opinion toward a city government in Tokyo metropolitan area. The method extracted clearly one important variable of “meeting places.” By examining carefully the public documents of the city, we found that the problem of “meeting places” in the city was considered to be one of the most serious financial problems. Thus, the finding by the gradual information maximization represents an important problem in the city.