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	Exploiting word cluster information for unsupervised feature selection
	Qingyao Wu; Yunming Ye; Michael Ng; Hanjing Su; Joshua Huang
	2010
会议名称	11th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2010
英文摘要	This paper presents an approach to integrate word clustering information into the process of unsupervised feature selection. In our scheme, the words in the whole feature space are clustered into groups based on the co-occurrence statistics of words. The resulted word clustering information and the bag-of-word information are combined together to measure the goodness of each word, which is our basic metric for selecting discriminative features. By exploiting word cluster information, we extend three well-known unsupervised feature selection methods and propose three new methods. A series of experiments are performed on three benchmark text data sets (the 20 Newsgroups, Reuters-21578 and CLASSIC3). The experimental results have shown that the new unsupervised feature selection methods can select more discriminative features, and in turn improve the clustering performance
收录类别	EI
语种	英语
内容类型	会议论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/3121]
专题	深圳先进技术研究院_数字所
作者单位	2010
推荐引用方式 GB/T 7714	Qingyao Wu,Yunming Ye,Michael Ng,et al. Exploiting word cluster information for unsupervised feature selection[C]. 见:11th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2010.

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