Instance-wise multi-view representation learning
文献类型：期刊论文
| 作者 | Li, Dan4 ; Wang, Haibao1; Wang, Yufeng4; Wang, Shengpei2,3
|
| 刊名 | INFORMATION FUSION
 |
| 出版日期 | 2023-03-01 |
| 卷号 | 91页码:612-622 |
| 关键词 | Multi-view representation learning Instance-wise selection View-specific View-shared |
| ISSN号 | 1566-2535 |
| DOI | 10.1016/j.inffus.2022.11.006 |
| 通讯作者 | Wang, Shengpei(wangshengpei2014@ia.ac.cn) |
| 英文摘要 | Multi-view representation learning aims to integrate multiple data information from different views to improve the task performance. The information contained in multi-view data is usually complex. Not only do different views contain different information, but also different samples of the same view contain different information. In the multi-view representation learning, most existing methods either simply treat each view/sample with equal importance, or set fixed or dynamic weights for different views/samples, which is not accurate enough to capture the information of dimensions of each sample and causes information redundancy, especially for high-dimensional samples. In this paper, we propose a novel unsupervised multi-view representation learning method based on instance-wise feature selection. A main advantage of instance-wise feature selection in this paper is that one can dynamically select dimensions that favor both view-specific representation learning and view-shared representation learning for each sample, thereby improving the performance from the perspective of model input. The proposed method consists of selector network, view-specific network and view-shared network. Specifically, selector network is used to obtain the selection template, which selects different number of dimensions conducive to representation learning from different samples to solve the sample heterogeneity problem; the view-specific network and view-shared network are used to extract the view-specific and view -shared representations, respectively. The selector network, view-shared network, and view-specific network are optimized alternately. Extensive experiments on various multi-view datasets with clustering and multi-label classification tasks demonstrate that the proposed method outperforms the state-of-the-art multi-view learning methods. |
| WOS关键词 | FEATURES |
| 资助项目 | National Natural Science Foundation of China[12101536] ; Natural Science Foundation of Shandong Province, China[ZR2022QF064] ; Natural Science Foundation of the Jiangsu Higher Education Institutions of China[21KJB510040] ; Open Fund project of Jiangsu Industrial Perception and Intelligent Manufacturing Equipment Engineering Research Center[ZK21-05-10] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000891912000006 |
| 出版者 | ELSEVIER |
| 资助机构 | National Natural Science Foundation of China ; Natural Science Foundation of Shandong Province, China ; Natural Science Foundation of the Jiangsu Higher Education Institutions of China ; Open Fund project of Jiangsu Industrial Perception and Intelligent Manufacturing Equipment Engineering Research Center |
| 源URL | [http://ir.ia.ac.cn/handle/173211/51311]  |
| 专题 | 脑图谱与类脑智能实验室 |
| 通讯作者 | Wang, Shengpei |
| 作者单位 | 1.Kyoto Univ, Grad Sch Informat, Dept Intelligent Sci & Technol, Kyoto, Japan 2.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China 3.Chinese Acad Sci, Res Ctr Brain Inspired Intelligence, Beijing 100190, Peoples R China 4.Yantai Univ, Sch Math & Informat Sci, Yantai, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Dan,Wang, Haibao,Wang, Yufeng,et al. Instance-wise multi-view representation learning[J]. INFORMATION FUSION,2023,91:612-622. |
| APA | Li, Dan,Wang, Haibao,Wang, Yufeng,&Wang, Shengpei.(2023).Instance-wise multi-view representation learning.INFORMATION FUSION,91,612-622. |
| MLA | Li, Dan,et al."Instance-wise multi-view representation learning".INFORMATION FUSION 91(2023):612-622. |
入库方式： OAI收割
来源：自动化研究所
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