| Discrete Nonnegative Spectral Clustering | |
| Yang, Yang1,2; Shen, Fumin1,2; Huang, Zi3; Shen, Heng Tao1,2; Li, Xuelong4; Shen, HT | |
| 作者部门 | 光学影像学习与分析中心 |
| 2017-09-01 | |
| 发表期刊 | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
 |
| ISSN | 1041-4347 |
| 卷号 | 29期号:9页码:1834-1845 |
| 产权排序 | 4 |
| 摘要 | Spectral clustering has been playing a vital role in various research areas. Most traditional spectral clustering algorithms comprise two independent stages (e.g., first learning continuous labels and then rounding the learned labels into discrete ones), which may cause unpredictable deviation of resultant cluster labels from genuine ones, thereby leading to severe information loss and performance degradation. In this work, we study how to achieve discrete clustering as well as reliably generalize to unseen data. We propose a novel spectral clustering scheme which deeply explores cluster label properties, including discreteness, nonnegativity, and discrimination, as well as learns robust out-of-sample prediction functions. Specifically, we explicitly enforce a discrete transformation on the intermediate continuous labels, which leads to a tractable optimization problem with a discrete solution. Besides, we preserve the natural nonnegative characteristic of the clustering labels to enhance the interpretability of the results. Moreover, to further compensate the unreliability of the learned clustering labels, we integrate an adaptive robust module with l(2,p) loss to learn prediction function for grouping unseen data. We also show that the out-of-sample component can inject discriminative knowledge into the learning of cluster labels under certain conditions. Extensive experiments conducted on various data sets have demonstrated the superiority of our proposal as compared to several existing clustering approaches. |
| 文章类型 | Article |
| 关键词 | Discrete Optimization Spectral Clustering Nonnegative |
| 学科领域 | Computer Science, Artificial Intelligence |
| WOS标题词 | Science & Technology ; Technology |
| DOI | 10.1109/TKDE.2017.2701825 |
| 收录类别 | SCI |
| 关键词[WOS] | IMAGE SEGMENTATION ; SEARCH |
| 语种 | 英语 |
| WOS研究方向 | Computer Science ; Engineering |
| 项目资助者 | National Natural Science Foundation of China(61572108 ; National Thousand-Young-Talents Program of China ; Fundamental Research Funds for the Central Universities(ZYGX2014Z007 ; 61632007 ; ZYGX2015J055) ; 61502081) |
| WOS类目 | Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic |
| WOS记录号 | WOS:000407433900005 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.opt.ac.cn/handle/181661/29217 |
| 专题 | 光谱成像技术研究室 |
| 通讯作者 | Shen, HT |
| 作者单位 | 1.Univ Elect Sci & Technol China, Ctr Future Media, Chengdu Shi 610051, Peoples R China 2.Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu Shi 610051, Peoples R China 3.Univ Queensland, Sch Informat Technol & Elect Engn, St Lucia, Qld 4072, Australia 4.Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Ctr OPT IMagery Anal & Learning OPTIMAL, Xian 710119, Shaanxi, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yang, Yang,Shen, Fumin,Huang, Zi,et al. Discrete Nonnegative Spectral Clustering[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2017,29(9):1834-1845. |
| APA | Yang, Yang,Shen, Fumin,Huang, Zi,Shen, Heng Tao,Li, Xuelong,&Shen, HT.(2017).Discrete Nonnegative Spectral Clustering.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,29(9),1834-1845. |
| MLA | Yang, Yang,et al."Discrete Nonnegative Spectral Clustering".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 29.9(2017):1834-1845. |
| 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 | ||
| Discrete Nonnegative（1370KB） | 期刊论文 | 出版稿 | 限制开放 | CC BY-NC-SA | 请求全文 | |
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