Re-Weighted Discriminatively Embedded K-Means for Multi-View Clustering | |
Xu, Jinglin1; Han, Junwei1; Nie, Feiping2,3; Li, Xuelong4; Xu, Jinglin (xujinglinlove@gmail.com) | |
作者部门 | 光学影像学习与分析中心 |
2017-06-01 | |
发表期刊 | IEEE TRANSACTIONS ON IMAGE PROCESSING |
ISSN | 1057-7149 |
卷号 | 26期号:6页码:3016-3027 |
产权排序 | 3 |
摘要 | Recent years, more and more multi-view data are widely used in many real-world applications. This kind of data (such as image data) is high dimensional and obtained from different feature extractors, which represents distinct perspectives of the data. How to cluster such data efficiently is a challenge. In this paper, we propose a novel multi-view clustering framework, called re-weighted discriminatively embedded K-means, for this task. The proposed method is a multi-view least-absolute residual model, which induces robustness to efficiently mitigates the influence of outliers and realizes dimension reduction during multi-view clustering. Specifically, the proposed model is an unsupervised optimization scheme, which utilizes iterative re-weighted least squares to solve least-absolute residual and adaptively controls the distribution of multiple weights in a re-weighted manner only based on its own low-dimensional subspaces and a common clustering indicator matrix. Furthermore, theoretical analysis (including optimality and convergence analysis) and the optimization algorithm are also presented. Compared with several state-of-the-art multi-view clustering methods, the proposed method substantially improves the accuracy of the clustering results on widely used benchmark data sets, which demonstrates the superiority of the proposed work. |
文章类型 | Article |
关键词 | Multi-view Clustering Discriminatively Embedded K-means Low-dimensional Subspace Iterative Re-weighted Least Squares |
WOS标题词 | Science & Technology ; Technology |
DOI | 10.1109/TIP.2017.2665976 |
收录类别 | SCI ; EI |
关键词[WOS] | RECOGNITION ; SCALE |
语种 | 英语 |
WOS研究方向 | Computer Science ; Engineering |
项目资助者 | National Science Foundation of China(61473231 ; 61522207) |
WOS类目 | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000401296100033 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.opt.ac.cn/handle/181661/28918 |
专题 | 光谱成像技术研究室 |
通讯作者 | Xu, Jinglin (xujinglinlove@gmail.com) |
作者单位 | 1.Northwestern Polytech Univ, Sch Automat, Xian 710072, Peoples R China 2.Northwestern Polytech Univ, Sch Comp Sci, Xian 710072, Peoples R China 3.Northwestern Polytech Univ, Ctr OPT IMagery Anal & Learning, Xian 710072, Peoples R China 4.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Ctr OPT IMagery Anal & Learning, State Key Lab Transient Opt & Photon, Xian 710119, Peoples R China |
推荐引用方式 GB/T 7714 | Xu, Jinglin,Han, Junwei,Nie, Feiping,et al. Re-Weighted Discriminatively Embedded K-Means for Multi-View Clustering[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2017,26(6):3016-3027. |
APA | Xu, Jinglin,Han, Junwei,Nie, Feiping,Li, Xuelong,&Xu, Jinglin .(2017).Re-Weighted Discriminatively Embedded K-Means for Multi-View Clustering.IEEE TRANSACTIONS ON IMAGE PROCESSING,26(6),3016-3027. |
MLA | Xu, Jinglin,et al."Re-Weighted Discriminatively Embedded K-Means for Multi-View Clustering".IEEE TRANSACTIONS ON IMAGE PROCESSING 26.6(2017):3016-3027. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Re-weighted discrimi(3000KB) | 期刊论文 | 作者接受稿 | 限制开放 | CC BY-NC-SA | 请求全文 |
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