Knowledge Commons of Institute of Automation，CAS
| Partially Shared Latent Factor Learning With Multiview Data | |
Liu, Jing1 ; Jiang, Yu1; Li, Zechao2; Zhou, Zhi-Hua3; Lu, Hanqing1
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| 发表期刊 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
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| 2015-06-01 | |
| 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 | 紫东太初大模型研究中心_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 |
| 第一作者单位 | 模式识别国家重点实验室 |
| 推荐引用方式 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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| Partially Shared Lat（2872KB） | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 | |
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