高维超球聚类的频率敏感竞争学习

Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290) Pub Date : 2002-08-07 DOI:10.1109/IJCNN.2002.1007755

A. Banerjee, Joydeep Ghosh

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引用次数: 48

摘要

本文从第一原理出发，导出了三种竞争学习机制，在输入和代表都归一化的情况下获得可比较大小的聚类。这些机制在实现高维空间输入的平衡分组方面非常有效，分别在26,099和21,839维空间中对两个流行的文本数据集进行聚类的实验结果说明了这一点。

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Frequency sensitive competitive learning for clustering on high-dimensional hyperspheres

This paper derives three competitive learning mechanisms from first principles to obtain clusters of comparable sizes when both inputs and representatives are normalized. These mechanisms are very effective in achieving balanced grouping of inputs in high dimensional spaces as illustrated by experimental results on clustering two popular text data sets in 26,099 and 21,839 dimensional spaces, respectively.

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Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290)

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