Joint Deep Multi-View Learning for Image Clustering

doi:10.1109/TKDE.2020.2973981

CORC > 自动化研究所 > 中国科学院自动化研究所 > 精密感知与控制研究中心 > 人工智能与机器学习

	Joint Deep Multi-View Learning for Image Clustering
	Xie, Yuan 1; Lin, Bingqian 2; Qu, Yanyun 3; Li, Cuihua 3; Zhang, Wensheng 4; Ma, Lizhuang 1; Wen, Yonggang 5,6; Tao, Dacheng 7
刊名	IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
	2021-11-01
卷号	33 期号:11 页码:3594-3606
关键词	Clustering methods Feature extraction Electronic mail Correlation Learning systems Clustering algorithms Machine learning Multi-view clustering deep learning multi-view fusion
ISSN号	1041-4347
DOI	10.1109/TKDE.2020.2973981
通讯作者	Qu, Yanyun(yyqu@xmu.edu.cn)
英文摘要	In this paper, a novel Deep Multi-view Joint Clustering (DMJC) framework is proposed, where multiple deep embedded features, multi-view fusion mechanism, and clustering assignments can be learned simultaneously. Through the joint learning strategy, the clustering-friendly multi-view features and useful multi-view complementary information can be exploited effectively to improve the clustering performance. Under the proposed joint learning framework, we design two ingenious variants of deep multi-view joint clustering models, whose multi-view fusion is implemented by two kinds of simple yet effective schemes. The first model, called DMJC-S, performs multi-view fusion in an implicit way via a novel multi-view soft assignment distribution. The second model, termed DMJC-T, defines a novel multi-view auxiliary target distribution to conduct the multi-view fusion explicitly. Both DMJC-S and DMJC-T are optimized under a KL divergence objective. Experiments on eight challenging image datasets demonstrate the superiority of both DMJC-S and DMJC-T over single/multi-view baselines and the state-of-the-art multi-view clustering methods, which proves the effectiveness of the proposed DMJC framework. To the best of our knowledge, this is the first work to model the multi-view clustering in a deep joint framework, which will provide a meaningful thinking in unsupervised multi-view learning.
资助项目	National Natural Science Foundation of China[61772524] ; National Natural Science Foundation of China[61772525] ; National Natural Science Foundation of China[61876161] ; National Natural Science Foundation of China[61701235] ; National Natural Science Foundation of China[61373077] ; National Natural Science Foundation of China[61602482] ; Beijing Municipal Natural Science Foundation[4182067] ; Fundamental Research Funds for the CentralUniversities ; Shanghai Key Laboratory of Trustworthy Computing
WOS关键词	NETWORK ; SCALE
WOS研究方向	Computer Science ; Engineering
语种	英语
出版者	IEEE COMPUTER SOC
WOS记录号	WOS:000704109900009
资助机构	National Natural Science Foundation of China ; Beijing Municipal Natural Science Foundation ; Fundamental Research Funds for the CentralUniversities ; Shanghai Key Laboratory of Trustworthy Computing
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/45747]
专题	精密感知与控制研究中心_人工智能与机器学习
通讯作者	Qu, Yanyun
作者单位	1.East China Normal Univ, Sch Comp Sci & Technol, Shanghai 200241, Peoples R China 2.Sun Yat Sen Univ, Intelligent Syst Engn, Guangzhou 510006, Guangdong, Peoples R China 3.Xiamen Univ, Sch Informat Sci & Engn, Xiamen 361005, Fujian, Peoples R China 4.Chinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing 100190, Peoples R China 5.Nanyang Technol Univ, Sch Comp Sci & Engn, Singapore 639798, Singapore 6.Coll Engn, Singapore, Singapore 7.Univ Sydney, Sch Comp Sci, Fac Engn, 6 Cleveland St, Darlington, NSW 2008, Australia
推荐引用方式 GB/T 7714	Xie, Yuan,Lin, Bingqian,Qu, Yanyun,et al. Joint Deep Multi-View Learning for Image Clustering[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2021,33(11):3594-3606.
APA	Xie, Yuan.,Lin, Bingqian.,Qu, Yanyun.,Li, Cuihua.,Zhang, Wensheng.,...&Tao, Dacheng.(2021).Joint Deep Multi-View Learning for Image Clustering.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,33(11),3594-3606.
MLA	Xie, Yuan,et al."Joint Deep Multi-View Learning for Image Clustering".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 33.11(2021):3594-3606.