Exploiting word cluster information for unsupervised feature selection
文献类型：会议论文
| 作者 | Qingyao Wu; Yunming Ye; Michael Ng; Hanjing Su; Joshua Huang |
| 出版日期 | 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 |
| 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. |
入库方式： OAI收割
来源：深圳先进技术研究院
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