Discrete Nonnegative Spectral Clustering

doi:10.1109/TKDE.2017.2701825

OPT OpenIR > 光谱成像技术研究室

	Discrete Nonnegative Spectral Clustering
	Yang, Yang 1,2; Shen, Fumin 1,2; Huang, Zi 3; Shen, Heng Tao 1,2; Li, Xuelong 4; 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	被引频次：65[WOS] [WOS记录] [WOS相关记录]
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.

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