Hierarchical multi-view metric learning with HSIC regularization

doi:10.1016/j.neucom.2022.09.073

CORC > 自动化研究所 > 中国科学院自动化研究所 > 智能感知与计算研究中心

	Hierarchical multi-view metric learning with HSIC regularization
	Deng, Huiyuan 2; Meng, Xiangzhu 1; Wang, Huibing 3; Feng, Lin 2
刊名	NEUROCOMPUTING
	2022-10-21
卷号	510 页码:135-148
关键词	Metric learning Multi -view learning Face verification Kinship verification Person re -identification
ISSN号	0925-2312
DOI	10.1016/j.neucom.2022.09.073
通讯作者	Feng, Lin(fenglin@dlut.edu.cn)
英文摘要	As the information era develops rapidly, it's common to utilize multiple features from different sources to represent one object. Measuring the similarity between multi-view objects is the fundamental task in multi-view learning. To effectively measure the similarity between multi-view samples, multi-view metric learning has gained extensive attention recently. Nevertheless, most existing methods merely focus on the closeness of similar pairs and the separability of dissimilar ones inside each view, so that rich consensus properties existing in multi-views data might be ignored to some extent. To mitigate this issue, we come up with a novel method entitled Hierarchical Multi-view Metric learning with HSIC regularization ((HMH)-H-2). (HMH)-H-2 aims to simultaneously maintain the closeness of similar points and the separability of dissimilar ones in intra-view and inter-view. Since multiple views depict different perspectives of the same object, the shared metric is introduced to capture the consensus information among those views. Moreover, we take advantage of the Hilbert-Schmidt Independence Criterion to seek the maximum distribution agreement of the multi-view dataset. Correspondingly, an algorithm based on Alternating Direction Method is provided to solve the proposed HM2H. Finally, various experimental results on five visual recognition datasets confirm the effectiveness and feasibility of our proposed method. (C) 2022 Elsevier B.V. All rights reserved.
资助项目	National Natural Science Foundation of PR China[61972064] ; LiaoNing Revitalization Talents Program[XLYC1806006] ; Fundamental Research Funds for the Central Universities[DUT19RC (3) 012]
WOS关键词	DEPENDENCE ; FACE
WOS研究方向	Computer Science
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:000862258000012
资助机构	National Natural Science Foundation of PR China ; LiaoNing Revitalization Talents Program ; Fundamental Research Funds for the Central Universities
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/50425]
专题	自动化研究所_智能感知与计算研究中心
通讯作者	Feng, Lin
作者单位	1.Chinese Acad Sci, Inst Automat, Ctr Res Intelligent Percept & Comp, Beijing, Peoples R China 2.Dalian Univ Technol, Sch Innovat & Entrepreneurship, Dalian, Peoples R China 3.Dalian Maritime Univ, Informat Sci & Technol Coll, Dalian 116024, Peoples R China
推荐引用方式 GB/T 7714	Deng, Huiyuan,Meng, Xiangzhu,Wang, Huibing,et al. Hierarchical multi-view metric learning with HSIC regularization[J]. NEUROCOMPUTING,2022,510:135-148.
APA	Deng, Huiyuan,Meng, Xiangzhu,Wang, Huibing,&Feng, Lin.(2022).Hierarchical multi-view metric learning with HSIC regularization.NEUROCOMPUTING,510,135-148.
MLA	Deng, Huiyuan,et al."Hierarchical multi-view metric learning with HSIC regularization".NEUROCOMPUTING 510(2022):135-148.