期刊
NEUROCOMPUTING
卷 275, 期 -, 页码 2855-2863出版社
ELSEVIER
DOI: 10.1016/j.neucom.2017.11.061
关键词
Feature selection; Co-regularized learning; Data distribution; Data reconstruction
资金
- National Program on Key Basic Research Project [2013CB329304]
- National Natural Science Foundation of China [61502332, 61432011, 61222210]
- Natural Science Foundation of Tianjin [17JCZDJC30800]
Unsupervised feature selection (UFS) is very challenging due to the lack of label information. Most UFS methods generate pseudo labels by spectral clustering, matrix factorization or dictionary learning, and convert UFS into a supervised feature selection problem. Generally, the features that can preserve the data distribution (i.e., cluster indicator matrix) should be selected. Under the matrix factorization framework, when the cluster indicator matrix is orthogonal, the cluster base (center) matrix can be used to select features that well reconstruct the data. However, almost all the UFS algorithms only select features that can either well preserve the cluster structure or reconstruct the data. In this paper, we propose a novel co-regularized unsupervised feature selection (CUFS) algorithm. Joint l(2,1)-norm co-regularization is imposed on multiple feature selection matrices to ensure that the selected features can both preserve data distribution and data reconstruction. Extensive experiments on benchmark datasets show that CUFS is superior to the state-of-the-art UFS algorithms. (c) 2017 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据