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题名:
Refined-Graph Regularization-Based Nonnegative Matrix Factorization
作者: Li, Xuelong1; Cui, Guosheng1,2; Dong, Yongsheng1,3
作者部门: 光学影像学习与分析中心
刊名: ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
出版日期: 2017-10-01
卷号: 9, 期号:1
关键词: Data representation ; refined-graph ; nonnegative matrix factorization (NMF) ; least squares regression ; image clustering
DOI: 10.1145/3090312
通讯作者: Dong, Yongsheng (dongyongsheng98@163.com)
文章类型: Article
英文摘要:

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.

WOS标题词: Science & Technology ; Technology
类目[WOS]: Computer Science, Artificial Intelligence ; Computer Science, Information Systems
研究领域[WOS]: Computer Science
关键词[WOS]: NONLINEAR DIMENSIONALITY REDUCTION ; GEOMETRIC FRAMEWORK ; REPRESENTATION
收录类别: SCI ; EI
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)
语种: 英语
WOS记录号: WOS:000414316900001
ISSN号: 2157-6904
产权排序: 1
Citation statistics:
内容类型: 期刊论文
URI标识: http://ir.opt.ac.cn/handle/181661/29251
Appears in Collections:光学影像学习与分析中心_期刊论文

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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

Recommended Citation:
Li, Xuelong,Cui, Guosheng,Dong, Yongsheng. Refined-Graph Regularization-Based Nonnegative Matrix Factorization[J]. ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY,2017-10-01,9(1).
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文件名: Refined-graph regularization-based Nonnegative matrix factorization.pdf
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