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| Joint clustering and feature selection | |
| Du, Liang (1); Shen, Yi-Dong (1) | |
| 2013 | |
| Conference Name | 14th International Conference on Web-Age Information Management, WAIM 2013 |
| Pages | 241-252 |
| Conference Date | June 14, 2013 - June 16, 2013 |
| Conference Place | Beidaihe, China |
| Indexed Type | EI |
| Publish Place | Springer Verlag, Tiergartenstrasse 17, Heidelberg, D-69121, Germany |
| ISSN | 3029743 |
| ISBN | 9783642385612 |
| Department | (1) State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China; (2) Graduate University, Chinese Academy of Sciences, China; (3) University of Chinese Academy of Sciences, Beijing 100049, China |
| English Abstract | Due to the absence of class labels, unsupervised feature selection is much more difficult than supervised feature selection. Traditional unsupervised feature selection algorithms usually select features to preserve the structure of the data set. Inspired from the recent developments on discriminative clustering, we propose in this paper a novel unsupervised feature selection approach via Joint Clustering and Feature Selection (JCFS). Specifically, we integrate Fisher score into the clustering framework. We select those features such that the fisher criterion is maximized and the manifold structure can be best preserved simultaneously. We also discover the connection between JCFS and other clustering and feature selection methods, such as discriminative K-means, JELSR and DCS. Experimental results on real world data sets demonstrated the effectiveness of the proposed algorithm. © 2013 Springer-Verlag Berlin Heidelberg.; Due to the absence of class labels, unsupervised feature selection is much more difficult than supervised feature selection. Traditional unsupervised feature selection algorithms usually select features to preserve the structure of the data set. Inspired from the recent developments on discriminative clustering, we propose in this paper a novel unsupervised feature selection approach via Joint Clustering and Feature Selection (JCFS). Specifically, we integrate Fisher score into the clustering framework. We select those features such that the fisher criterion is maximized and the manifold structure can be best preserved simultaneously. We also discover the connection between JCFS and other clustering and feature selection methods, such as discriminative K-means, JELSR and DCS. Experimental results on real world data sets demonstrated the effectiveness of the proposed algorithm. © 2013 Springer-Verlag Berlin Heidelberg. |
| Language | 英语 |
| Content Type | 会议论文 |
| URI | http://ir.iscas.ac.cn/handle/311060/16671 |
| Collection | 中国科学院软件研究所 |
| Recommended Citation GB/T 7714 | Du, Liang ,Shen, Yi-Dong . Joint clustering and feature selection[C]. Springer Verlag, Tiergartenstrasse 17, Heidelberg, D-69121, Germany,2013:241-252. |
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