Multiview clustering via nonnegative matrix factorization based on graph agreement | |
Zhang, Chengfeng1; Fu, Wenjun1; Wang, Guanglong2; Shi, Lei2; Meng, Xiangzhu3 | |
发表期刊 | JOURNAL OF ELECTRONIC IMAGING |
ISSN | 1017-9909 |
2022-07-01 | |
卷号 | 31期号:4页码:18 |
通讯作者 | Meng, Xiangzhu(xiangzhu.meng@cripac.ia.ac.cn) |
摘要 | With the rapid development of information technology, one object can usually be described by multiple views. Intuitively, the diversity and complementarity of different views provide a more comprehensive data description, which can lead to better performance on multiview clustering tasks. Motivated by the fact that how to fully discover diversity and complementary information across views is the key point to deal with multiview clustering, we propose a multiview learning method, termed as graph-agreement nonnegative matrix factorization (GANMF). GANMF attempts to implement this goal based on matrix factorization technology while exploiting the rich multiview information based on the intra- and interview aspects. Specifically, the graph agreement between representation space and raw space is maximized to preserve the intrinsic geometric property in individual view for the intraview case. Similarly, the graph structures in different views are expected to keep consistent with each other by minimizing the divergence between pairwise views. To this end, the intrinsic information and geometric structure information in the intraview case and complementary and compatibility information in the interview case can be simultaneously formulated into one framework. To solve the proposed GANMF, we correspondingly develop an effective algorithm based on iterative alternating strategy. Extensive experimental results on seven multiview datasets demonstrate the superiority and effectiveness of our proposed method. |
关键词 | multiview clustering nonnegative matrix factorization graph mechanism |
DOI | 10.1117/1.JEI.31.4.043024 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of PRChina[61672130] ; National Natural Science Foundation of PRChina[72001191] |
项目资助者 | National Natural Science Foundation of PRChina |
WOS研究方向 | Engineering ; Optics ; Imaging Science & Photographic Technology |
WOS类目 | Engineering, Electrical & Electronic ; Optics ; Imaging Science & Photographic Technology |
WOS记录号 | WOS:000848751400042 |
出版者 | SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/50030 |
专题 | 智能感知与计算研究中心 |
通讯作者 | Meng, Xiangzhu |
作者单位 | 1.Beijing China Coal Mine Engn Co Ltd, Beijing, Peoples R China 2.Yanchang Petr Barasu Coal Ind Co Ltd, Yulin, Peoples R China 3.Chinese Acad Sci, Ctr Res Intelligent Percept & Comp, Inst Automat, Beijing, Peoples R China |
推荐引用方式 GB/T 7714 | Zhang, Chengfeng,Fu, Wenjun,Wang, Guanglong,et al. Multiview clustering via nonnegative matrix factorization based on graph agreement[J]. JOURNAL OF ELECTRONIC IMAGING,2022,31(4):18. |
APA | Zhang, Chengfeng,Fu, Wenjun,Wang, Guanglong,Shi, Lei,&Meng, Xiangzhu.(2022).Multiview clustering via nonnegative matrix factorization based on graph agreement.JOURNAL OF ELECTRONIC IMAGING,31(4),18. |
MLA | Zhang, Chengfeng,et al."Multiview clustering via nonnegative matrix factorization based on graph agreement".JOURNAL OF ELECTRONIC IMAGING 31.4(2022):18. |
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