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Refined-Graph Regularization-Based Nonnegative Matrix Factorization
Li, Xuelong1; Cui, Guosheng1,2; Dong, Yongsheng1,3; Dong, Yongsheng (dongyongsheng98@163.com)
作者部门光学影像学习与分析中心
2017-10-01
发表期刊ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
ISSN2157-6904
卷号9期号:1
产权排序1
摘要

Nonnegative matrix factorization (NMF) is one of the most popular data representation methods in the field of computer vision and pattern recognition. High-dimension data are usually assumed to be sampled fromthe submanifold embedded in the original high-dimension space. To preserve the locality geometric structure of the data, k-nearest neighbor (k-NN) graph is often constructed to encode the near-neighbor layout structure. However, k-NN graph is based on Euclidean distance, which is sensitive to noise and outliers. In this article, we propose a refined-graph regularized nonnegative matrix factorization by employing a manifold regularized least-squares regression (MRLSR) method to compute the refined graph. In particular, each sample is represented by the whole dataset regularized with l(2)-norm and Laplacian regularizer. Then a MRLSR graph is constructed based on the representative coefficients of each sample. Moreover, we present two optimization schemes to generate refined-graphs by employing a hard-thresholding technique. We further propose two refined-graph regularized nonnegative matrix factorization methods and use them to perform image clustering. Experimental results on several image datasets reveal that they outperform 11 representative methods.

文章类型Article
关键词Data Representation Refined-graph Nonnegative Matrix Factorization (Nmf) Least Squares Regression Image Clustering
WOS标题词Science & Technology ; Technology
DOI10.1145/3090312
收录类别SCI ; EI
关键词[WOS]NONLINEAR DIMENSIONALITY REDUCTION ; GEOMETRIC FRAMEWORK ; REPRESENTATION
语种英语
WOS研究方向Computer Science
项目资助者National Natural Science Foundation of China(61761130079 ; International Science and Technology Cooperation Project of Henan Province(162102410021) ; State Key Laboratory of Virtual Reality Technology and Systems(BUAA-VR-16KF-04) ; Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province(GD201605) ; U1604153)
WOS类目Computer Science, Artificial Intelligence ; Computer Science, Information Systems
WOS记录号WOS:000414316900001
引用统计
文献类型期刊论文
条目标识符http://ir.opt.ac.cn/handle/181661/29251
专题光学影像学习与分析中心
通讯作者Dong, Yongsheng (dongyongsheng98@163.com)
作者单位1.Chinese Acad Sci, Ctr OPT IMagery Anal & Learning OPTIMAL, State Key Lab Transient Opt & Photon, Xian Inst Opt & Precis Mech, Xian 710119, Shaanxi, Peoples R China
2.Univ Chinese Acad Sci, 19A Yuquanlu, Beijing 100049, Peoples R China
3.Henan Univ Sci & Technol, Sch Informat Engn, Luoyang 471023, Henan, Peoples R China
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Li, Xuelong,Cui, Guosheng,Dong, Yongsheng,et al. Refined-Graph Regularization-Based Nonnegative Matrix Factorization[J]. ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY,2017,9(1).
APA Li, Xuelong,Cui, Guosheng,Dong, Yongsheng,&Dong, Yongsheng .(2017).Refined-Graph Regularization-Based Nonnegative Matrix Factorization.ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY,9(1).
MLA Li, Xuelong,et al."Refined-Graph Regularization-Based Nonnegative Matrix Factorization".ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY 9.1(2017).
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