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Bilevel Multiview Latent Space Learning

文献类型：期刊论文

作者Xue, Zhe3,5; Li, Guorong3,5; Wang, Shuhui4; Zhang, Weigang1,2; Huang, Qingming3,4,5
刊名IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
出版日期2018-02-01
卷号28期号:2页码:327-341
关键词Image and video classification latent space matrix factorization multiview
ISSN号1051-8215
DOI10.1109/TCSVT.2016.2607842
英文摘要Different kinds of features describe different aspects of image data, and each feature can be treated as a view when we take it as a particular understanding of images. Leveraging multiple views provides a richer and comprehensive description than using only a single view. However, multiview data are often represented by high-dimensional heterogeneous features, so it is meaningful to find a low-dimensional consensus representation from multiple views. In this paper, we propose an unsupervised multiview dimensionality reduction method for images based on bilevel latent space learning. As different views have different physical meanings and statistical properties, they are not directly comparable. Therefore, we learn the comparable representation for each view in the first level. The shared and the private nature of multiview data are exploited to accurately preserve the information of each view. Then, we fuse different views into a low-dimensional representation by conducting joint matrix factorization in the second level. To guarantee the low-dimensional representation to be compact and discriminative, the intrinsic geometric structure of data is utilized. Besides, our method considers resisting the outliers and noise contained in multiview data, which may influence the learned representation and deteriorate its semantic consistency. We design appropriate optimization objectives to learn the latent spaces in different levels. Compared with the existing methods, our method could provide a more flexible multiview learning strategy that not only accurately captures the information of each view but also is robust to outliers and noise, which can obtain a more discriminative and compact low-dimensional representation. Experiments on two real-world image data sets demonstrate the advantages of our method over the existing multiview dimensionality reduction methods.
资助项目National Basic Research Program of China (973 Program)[2012CB316400] ; National Basic Research Program of China (973 Program)[2015CB351802] ; National Natural Science Foundation of China[61303153] ; National Natural Science Foundation of China[61332016] ; National Natural Science Foundation of China[61620106009] ; National Natural Science Foundation of China[61572488] ; National Natural Science Foundation of China[61672497] ; 863 program of China[2014AA015202] ; Bureau of Frontier Sciences and Education (CAS)[QYZDJ-SSW-SYS013]
WOS研究方向Engineering
语种英语
WOS记录号WOS:000425036400005
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/5625]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Li, Guorong; Huang, Qingming
作者单位1.Univ Chinese Acad Sci, Chinese Acad Sci, Beijing 100049, Peoples R China
2.Harbin Inst Technol, Sch Comp Sci & Technol, Weihai 264209, Peoples R China
3.Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing 101408, Peoples R China
4.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
5.UCAS, Key Lab Big Data Min & Knowledge Management, Beijing 101408, Peoples R China
推荐引用方式
GB/T 7714		 Xue, Zhe,Li, Guorong,Wang, Shuhui,et al. Bilevel Multiview Latent Space Learning[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2018,28(2):327-341.
APA Xue, Zhe,Li, Guorong,Wang, Shuhui,Zhang, Weigang,&Huang, Qingming.(2018).Bilevel Multiview Latent Space Learning.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,28(2),327-341.
MLA Xue, Zhe,et al."Bilevel Multiview Latent Space Learning".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 28.2(2018):327-341.

入库方式： OAI收割

来源：计算技术研究所

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