Discrete Nonnegative Spectral Clustering | |
Yang, Yang1,2; Shen, Fumin1,2; Huang, Zi3; Shen, Heng Tao1,2; Li, Xuelong4 | |
刊名 | ieee transactions on knowledge and data engineering |
2017-09-01 | |
卷号 | 29期号:9页码:1834-1845 |
关键词 | Discrete optimization spectral clustering nonnegative |
ISSN号 | 1041-4347 |
通讯作者 | shen, ht |
产权排序 | 4 |
英文摘要 | 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. |
学科主题 | computer science, artificial intelligence ; computer science, information systems ; engineering, electrical & electronic |
研究领域[WOS] | computer science ; engineering |
收录类别 | SCI |
语种 | 英语 |
WOS记录号 | WOS:000407433900005 |
内容类型 | 期刊论文 |
源URL | [http://ir.opt.ac.cn/handle/181661/29217] |
专题 | 西安光学精密机械研究所_光学影像学习与分析中心 |
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.(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. |
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