Progressive Feature Extraction with a Greedy Network-growing Algorithm
Progressive Feature Extraction with a Greedy Network-growing Algorithm
复制标题
使用贪心网络增长算法进行渐进式特征提取
DOI：
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发表时间：
2003
期刊：
影响因子：
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通讯作者：
R. Kamimura
中科院分区：
文献类型：
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作者：
R. Kamimura
本文提出了一种新的信息理论方法——贪婪网络生长算法。这种方法被称为“贪婪”，因为使用这种算法的网络在增长的同时，会从外部吸收尽可能多的信息。该方法以信息论竞争学习为基础，解决了竞争学习中存在的神经元死亡和神经元数量不合理等基本问题。新模型可以通过反复最大化信息内容和逐渐从输入模式中提取显著特征来增长网络。由于新模型可以在学习的早期处理不适当的特征检测，提取的特征应该覆盖大部分的输入模式。将该方法应用于政治数据分析、医学数据分析和信息科学教育分析。实验结果表明，该方法可以获得重要的信息，并且可以提取更显式的特征。
In this paper, a new information theoretic method called the greedy network-growing algorithm is proposed. The method is called “greedy,” because a network with this algorithm grows while absorbing as much information as possible from outside. The method is based upon information theoretic competitive learning and can solve the fundamental problems inherent in competitive learning, such as the dead neurons and inappropriate number of neurons problems. The new model can grow networks by repeatedly maximizing information content and by gradually extracting salient features from input patterns. Because the new model can cope with inappropriate feature detection in the early stage of learning, extracted features should cover most of the input patterns. The new method is applied to political data analysis, medical data analysis, and information science education analysis. Experimental results confirm that the new method can acquire significant information and that more explicit features can be extracted.