标题:Adaptive collaborative similarity learning for unsupervised multi-view feature selection
作者:Dong, Xiao ;Zhu, Lei ;Song, Xuemeng ;Li, Jingjing ;Cheng, Zhiyong
通讯作者:Zhu, Lei
作者机构:[Dong, Xiao ;Zhu, Lei ] School of Information Science and Engineering, Shandong Normal University, China;[Li, Jingjing ] University of Electronic Scie 更多
会议名称:27th International Joint Conference on Artificial Intelligence, IJCAI 2018
会议日期:13 July 2018 through 19 July 2018
来源:IJCAI International Joint Conference on Artificial Intelligence
出版年:2018
卷:2018-July
页码:2064-2070
摘要:In this paper, we investigate the research problem of unsupervised multi-view feature selection. Conventional solutions first simply combine multiple pre-constructed view-specific similarity structures into a collaborative similarity structure, and then perform the subsequent feature selection. These two processes are separate and independent. The collaborative similarity structure remains fixed during feature selection. Further, the simple undirected view combination may adversely reduce the reliability of the ultimate similarity structure for feature selection, as the view-specific similarity structures generally involve noises and outlying entries. To alleviate these problems, we propose an adaptive collaborative similarity learning (ACSL) for multi-view feature selection. We propose to dynamically learn the collaborative similarity structure, and further integrate it with the ultimate feature selection into a unified framework. Moreover, a reasonable rank constraint is devised to adaptively learn an ideal collaborative similarity structure with proper similarity combination weights and desirable neighbor assignment, both of which could positively facilitate the feature selection. An effective solution guaranteed with the proved convergence is derived to iteratively tackle the formulated optimization problem. Experiments demonstrate the superiority of the proposed approach. © 2018 International Joint Conferences on Artificial Intelligence. All right reserved.
收录类别:EI;SCOPUS
Scopus被引频次:1
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85054207738&partnerID=40&md5=6fb9ce63f24324aed25409a85664bc7c
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