Joint clustering and feature selection

ISCAS OpenIR

	Joint clustering and feature selection
	Du, Liang (1); Shen, Yi-Dong (1)
	2013
会议名称	14th International Conference on Web-Age Information Management, WAIM 2013
页码	241-252
会议日期	June 14, 2013 - June 16, 2013
会议地点	Beidaihe, China
收录类别	EI
出版地	Springer Verlag, Tiergartenstrasse 17, Heidelberg, D-69121, Germany
ISSN	3029743
ISBN	9783642385612
部门归属	(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
摘要	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.
语种	英语
内容类型	会议论文
URI标识	http://ir.iscas.ac.cn/handle/311060/16671
专题	中国科学院软件研究所
推荐引用方式 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.