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Shared Low-Rank Correlation Embedding for Multiple Feature Fusion

文献类型：期刊论文

作者Wang, Zhan2; Wang, Lizhi2; Wan, Jun1,3; Huang, Hua2
刊名IEEE TRANSACTIONS ON MULTIMEDIA
出版日期2021
卷号23页码:1855-1867
关键词Correlation Kernel Task analysis Fuses Noise measurement Laplace equations Dictionaries Common subspace low-rank representation multiple feature fusion canonical correlation analysis
ISSN号1520-9210
DOI10.1109/TMM.2020.3003747
通讯作者Huang, Hua(huahuang@bit.edu.cn)
英文摘要The diversity of multimedia data in the real world usually forms heterogeneous types of feature sets. How to explore the structure information and the relationships among multiple features is still an open problem. In this paper, we propose an unsupervised subspace learning method, named the shared low-rank correlation embedding (SLRCE) for multiple feature fusion. First, in the learned subspace, we implement the low-rank representation on each feature set and enforce a shared low-rank constraint to uncover the common structure information of multiple features. Second, we develop an enhanced correlation analysis in the learned subspace for simultaneously removing the redundancy of each feature set and exploring the correlation of multiple features. Finally, we incorporate the shared low-rank representation and the correlation analysis into a unified framework. The shared low-rank constraint not only depicts the data distribution consistency among multiple features, but also assists robust subspace learning. Our method is robust to noise in practice and can be extended to the kernel case to handle the nonlinear feature fusion. Experimental results on several typical datasets demonstrate the superior performance of the proposed methods.
WOS关键词CANONICAL CORRELATION-ANALYSIS ; ALGORITHM ; KERNEL ; RECOGNITION
资助项目National Key Research and Development Program of China[2017YFB1002203]
WOS研究方向Computer Science ; Telecommunications
语种英语
WOS记录号WOS:000724477100004
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Key Research and Development Program of China
源URL[http://ir.ia.ac.cn/handle/173211/46557]  
专题自动化研究所_模式识别国家重点实验室_生物识别与安全技术研究中心
通讯作者Huang, Hua
作者单位1.Univ Chinese Acad Sci, Beijing 100190, Peoples R China
2.Beijing Inst Technol, Beijing Lab Intelligent Informat Technol, Sch Comp Sci & Technol, Beijing 100081, Peoples R China
3.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Zhan,Wang, Lizhi,Wan, Jun,et al. Shared Low-Rank Correlation Embedding for Multiple Feature Fusion[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2021,23:1855-1867.
APA Wang, Zhan,Wang, Lizhi,Wan, Jun,&Huang, Hua.(2021).Shared Low-Rank Correlation Embedding for Multiple Feature Fusion.IEEE TRANSACTIONS ON MULTIMEDIA,23,1855-1867.
MLA Wang, Zhan,et al."Shared Low-Rank Correlation Embedding for Multiple Feature Fusion".IEEE TRANSACTIONS ON MULTIMEDIA 23(2021):1855-1867.

入库方式： OAI收割

来源：自动化研究所

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