Hierarchical multi-view metric learning with HSIC regularization
文献类型：期刊论文
| 作者 | Deng, Huiyuan2; Meng, Xiangzhu3; Wang, Huibing1; Feng, Lin2 |
| 刊名 | NEUROCOMPUTING
 |
| 出版日期 | 2022-10-21 |
| 卷号 | 510页码:135-148 |
| 关键词 | Metric learning Multi -view learning Face verification Kinship verification Person re -identification |
| ISSN号 | 0925-2312 |
| DOI | 10.1016/j.neucom.2022.09.073 |
| 通讯作者 | Feng, Lin(fenglin@dlut.edu.cn) |
| 英文摘要 | As the information era develops rapidly, it's common to utilize multiple features from different sources to represent one object. Measuring the similarity between multi-view objects is the fundamental task in multi-view learning. To effectively measure the similarity between multi-view samples, multi-view metric learning has gained extensive attention recently. Nevertheless, most existing methods merely focus on the closeness of similar pairs and the separability of dissimilar ones inside each view, so that rich consensus properties existing in multi-views data might be ignored to some extent. To mitigate this issue, we come up with a novel method entitled Hierarchical Multi-view Metric learning with HSIC regularization ((HMH)-H-2). (HMH)-H-2 aims to simultaneously maintain the closeness of similar points and the separability of dissimilar ones in intra-view and inter-view. Since multiple views depict different perspectives of the same object, the shared metric is introduced to capture the consensus information among those views. Moreover, we take advantage of the Hilbert-Schmidt Independence Criterion to seek the maximum distribution agreement of the multi-view dataset. Correspondingly, an algorithm based on Alternating Direction Method is provided to solve the proposed HM2H. Finally, various experimental results on five visual recognition datasets confirm the effectiveness and feasibility of our proposed method. (C) 2022 Elsevier B.V. All rights reserved. |
| WOS关键词 | DEPENDENCE ; FACE |
| 资助项目 | National Natural Science Foundation of PR China[61972064] ; LiaoNing Revitalization Talents Program[XLYC1806006] ; Fundamental Research Funds for the Central Universities[DUT19RC (3) 012] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000862258000012 |
| 出版者 | ELSEVIER |
| 资助机构 | National Natural Science Foundation of PR China ; LiaoNing Revitalization Talents Program ; Fundamental Research Funds for the Central Universities |
| 源URL | [http://ir.ia.ac.cn/handle/173211/50425]  |
| 专题 | 自动化研究所_智能感知与计算研究中心 |
| 通讯作者 | Feng, Lin |
| 作者单位 | 1.Dalian Maritime Univ, Informat Sci & Technol Coll, Dalian 116024, Peoples R China 2.Dalian Univ Technol, Sch Innovat & Entrepreneurship, Dalian, Peoples R China 3.Chinese Acad Sci, Inst Automat, Ctr Res Intelligent Percept & Comp, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Deng, Huiyuan,Meng, Xiangzhu,Wang, Huibing,et al. Hierarchical multi-view metric learning with HSIC regularization[J]. NEUROCOMPUTING,2022,510:135-148. |
| APA | Deng, Huiyuan,Meng, Xiangzhu,Wang, Huibing,&Feng, Lin.(2022).Hierarchical multi-view metric learning with HSIC regularization.NEUROCOMPUTING,510,135-148. |
| MLA | Deng, Huiyuan,et al."Hierarchical multi-view metric learning with HSIC regularization".NEUROCOMPUTING 510(2022):135-148. |
入库方式： OAI收割
来源：自动化研究所
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