CASIA OpenIR  > 模式识别国家重点实验室  > 图像与视频分析
Partially Shared Latent Factor Learning With Multiview Data
Liu, Jing1; Jiang, Yu1; Li, Zechao2; Zhou, Zhi-Hua3; Lu, Hanqing1
Source PublicationIEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
2015-06-01
Volume26Issue:6Pages:1233-1246
SubtypeArticle
AbstractMultiview representations reveal the fundamental attributes of the studied instances from different perspectives. Some common perspectives are reviewed by multiple views simultaneously, while some specific ones are reflected by individual views. That is, there are two kinds of properties embedded in the multiview data: 1) consistency and 2) complementarity. Different from most multiview learning approaches only focusing on either consistency or complementarity, this paper proposes a novel semisupervised multiview learning algorithm, called partially shared latent factor (PSLF) learning, which jointly exploits both consistent and complementary information among multiple views. In PSLF, a nonnegative matrix factorization (NMF)-based formulation is adopted to learn a compact and comprehensive partially shared latent representation, which is composed of common latent factors shared by multiple views and some specific latent factors to each view. With the learned representations of multiview data, we introduce a robust sparse regression model to predict the cluster labels of labeled data. By integrating the NMF-based model and the regression model, we obtain a unified formulation and propose a multiplicative-based alternative algorithm for optimization. In addition, PSLF can learn the weights of different views adaptively according to the reconstruction precisions of data matrices. Our experimental study indicates different multiview data that contains consistent and complementary information in different degrees. In addition, the encouraging results of the proposed algorithm are achieved in comparison with the state-of-the-art algorithms on real-world data sets.
KeywordComplementarity Consistency Latent Factor Learning Multiview Learning Nonnegative Matrix Factorization (Nmf) Semisupervised Learning
WOS HeadingsScience & Technology ; Technology
WOS KeywordNONNEGATIVE MATRIX FACTORIZATION
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000354957000010
Citation statistics
Cited Times:30[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/7920
Collection模式识别国家重点实验室_图像与视频分析
Affiliation1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
2.Nanjing Univ Sci & Technol, Sch Comp Sci, Nanjing 210094, Jiangsu, Peoples R China
3.Nanjing Univ, Natl Key Lab Novel Software Technol, Nanjing 210093, Jiangsu, Peoples R China
Recommended Citation
GB/T 7714
Liu, Jing,Jiang, Yu,Li, Zechao,et al. Partially Shared Latent Factor Learning With Multiview Data[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2015,26(6):1233-1246.
APA Liu, Jing,Jiang, Yu,Li, Zechao,Zhou, Zhi-Hua,&Lu, Hanqing.(2015).Partially Shared Latent Factor Learning With Multiview Data.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,26(6),1233-1246.
MLA Liu, Jing,et al."Partially Shared Latent Factor Learning With Multiview Data".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 26.6(2015):1233-1246.
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