Partially Shared Latent Factor Learning With Multiview Data
Liu, Jing1; Jiang, Yu1; Li, Zechao2; Zhou, Zhi-Hua3; Lu, Hanqing1
2015-06-01
发表期刊IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
卷号26期号:6页码:1233-1246
文章类型Article
摘要Multiview 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.
关键词Complementarity Consistency Latent Factor Learning Multiview Learning Nonnegative Matrix Factorization (Nmf) Semisupervised Learning
WOS标题词Science & Technology ; Technology
关键词[WOS]NONNEGATIVE MATRIX FACTORIZATION
收录类别SCI
语种英语
WOS研究方向Computer Science ; Engineering
WOS类目Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号WOS:000354957000010
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/7920
专题模式识别国家重点实验室_图像与视频分析
作者单位1.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
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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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