Subspace clustering guided convex nonnegative matrix factorization

doi:10.1016/j.neucom.2018.02.067

OPT OpenIR > 光谱成像技术研究室

	Subspace clustering guided convex nonnegative matrix factorization
	Cui, Guosheng 1,2; Li, Xuelong 1; Dong, Yongsheng 1; Dong, YS (reprint author), Chinese Acad Sci, Xian Inst Opt & Precis Mech, Ctr OPT IMagery Anal & Learning OPTIMAL, Xian 710119, Shaanxi, Peoples R China.
作者部门	光学影像学习与分析中心
	2018-05-31
发表期刊	NEUROCOMPUTING
ISSN	0925-2312
卷号	292 页码:38-48
产权排序	1
摘要	As one of the most important information of the data, the geometry structure information is usually modeled by a similarity graph to enforce the effectiveness of nonnegative matrix factorization (NMF). However, pairwise distance based graph is sensitive to noise and can not capture the subspace structure of the data. Reconstruction coefficients based graph can capture the subspace structure of the data, but the procedure of building the representation based graph is usually independent to the framework of NMF. To address this issue, a novel subspace clustering guided convex nonnegative matrix factorization (SC-CNMF) is proposed. In this NMF framework, the nonnegative subspace clustering is incorporated to learning the representation based graph, and meanwhile, a convex nonnegative matrix factorization is also updated simultaneously. To tackle the noise influence of the dataset, only k largest entries of each representation are kept in the subspace clustering. To capture the complicated geometry structure of the data, multiple centroids are also introduced to describe each cluster. Additionally, a row constraint is used to remove the relevance among the rows of the encoding matrix, which can help to improve the clustering performance of the proposed model. For the proposed NMF framework, two different objective functions with different optimizing schemes are designed. Image clustering experiments are conducted to demonstrate the effectiveness of the proposed methods on several datasets and compared with some related works based on NMF together with k-means clustering method and PCA as baseline. (c) 2018 Elsevier B.V. All rights reserved.
文章类型	Article
关键词	Convex Nonnegative Matrix Factorization Subspace Clustering Multiple Centroids Geometry Structure Image Clustering
学科领域	Computer Science, Artificial Intelligence
WOS标题词	Science & Technology ; Technology
DOI	10.1016/j.neucom.2018.02.067
收录类别	SCI
关键词[WOS]	REPRESENTATION
语种	英语
WOS研究方向	Computer Science
项目资助者	National Natural Science Foundation of China(61761130079 ; Key Research Program of Frontier Sciences, CAS(QYZDY-SSW-JSC044) ; Training Program for the Young-Backbone Teachers in Universities of Henan Province(2017GGJS065) ; State Key Laboratory of Virtual Reality Technology and Systems(BUAAVR-16KF-04) ; U1604153)
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000429321400003
引用统计	被引频次：44[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/181661/30050
专题	光谱成像技术研究室
通讯作者	Dong, YS (reprint author), Chinese Acad Sci, Xian Inst Opt & Precis Mech, Ctr OPT IMagery Anal & Learning OPTIMAL, Xian 710119, Shaanxi, Peoples R China.
作者单位	1.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Ctr OPT IMagery Anal & Learning OPTIMAL, Xian 710119, Shaanxi, Peoples R China 2.Univ Chinese Acad Sci, 19A Yuquanlu, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Cui, Guosheng,Li, Xuelong,Dong, Yongsheng,et al. Subspace clustering guided convex nonnegative matrix factorization[J]. NEUROCOMPUTING,2018,292:38-48.
APA	Cui, Guosheng,Li, Xuelong,Dong, Yongsheng,&Dong, YS .(2018).Subspace clustering guided convex nonnegative matrix factorization.NEUROCOMPUTING,292,38-48.
MLA	Cui, Guosheng,et al."Subspace clustering guided convex nonnegative matrix factorization".NEUROCOMPUTING 292(2018):38-48.

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