Graph Structure Aware Contrastive Multi-View Clustering
文献类型：期刊论文
| 作者 | Chen, Rui1,2 ; Tang, Yongqiang2; Cai, Xiangrui3; Yuan, Xiaojie4; Feng, Wenlong1,5; Zhang, Wensheng1,2
|
| 刊名 | IEEE TRANSACTIONS ON BIG DATA
 |
| 出版日期 | 2024-06-01 |
| 卷号 | 10期号:3页码:260-274 |
| 关键词 | Correlation Semantics Big Data Representation learning Data models Data mining Analytical models Contrastive learning deep representation graph embedding multi-view clustering |
| ISSN号 | 2332-7790 |
| DOI | 10.1109/TBDATA.2023.3334674 |
| 通讯作者 | Tang, Yongqiang(yongqiang.tang@ia.ac.cn) |
| 英文摘要 | Multi-view clustering has become a research hotspot in recent decades because of its effectiveness in heterogeneous data fusion. Although a large number of related studies have been developed one after another, most of them usually only concern with the characteristics of the data themselves and overlook the inherent connection among samples, hindering them from exploring structural knowledge of graph space. Moreover, many current works tend to highlight the compactness of one cluster without taking the differences between clusters into account. To track these two drawbacks, in this article, we propose a graph structure aware contrastive multi-view clustering (namely, GCMC) approach. Specifically, we incorporate the well-designed graph autoencoder with conventional multi-layer perception autoencoder to extract the structural and high-level representation of multi-view data, so that the underlying correlation of samples can be effectively squeezed for model learning. Then the contrastive learning paradigm is performed on multiple pseudo-label distributions to ensure that the positive pairs of pseudo-label representations share the complementarity across views while the divergence between negative pairs is sufficiently large. This makes each semantic cluster more discriminative, i.e., jointly satisfying intra-cluster compactness and inter-cluster exclusiveness. Through comprehensive experiments on eight widely-known datasets, we prove that the proposed approach can perform better than the state-of-the-art opponents. |
| WOS关键词 | REPRESENTATION ; ALGORITHM |
| 资助项目 | National Key Research and Development Program of China |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:001224177900001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Key Research and Development Program of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/58484]  |
| 专题 | 精密感知与控制研究中心_人工智能与机器学习 |
| 通讯作者 | Tang, Yongqiang |
| 作者单位 | 1.Hainan Univ, Coll Informat Sci & Technol, Haikou 570228, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China 3.Nankai Univ, Coll Comp Sci, Tianjin 300350, Peoples R China 4.Nankai Univ, Coll Cyber Sci, Tianjin Key Lab Network & Data Secur Technol, Tianjin 300350, Peoples R China 5.Hainan Univ, State Key Lab Marine Resource Utilizat South China, Haikou 570228, Peoples R China |
| 推荐引用方式 GB/T 7714 | Chen, Rui,Tang, Yongqiang,Cai, Xiangrui,et al. Graph Structure Aware Contrastive Multi-View Clustering[J]. IEEE TRANSACTIONS ON BIG DATA,2024,10(3):260-274. |
| APA | Chen, Rui,Tang, Yongqiang,Cai, Xiangrui,Yuan, Xiaojie,Feng, Wenlong,&Zhang, Wensheng.(2024).Graph Structure Aware Contrastive Multi-View Clustering.IEEE TRANSACTIONS ON BIG DATA,10(3),260-274. |
| MLA | Chen, Rui,et al."Graph Structure Aware Contrastive Multi-View Clustering".IEEE TRANSACTIONS ON BIG DATA 10.3(2024):260-274. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。