Refined-Graph Regularization-Based Nonnegative Matrix Factorization | |
Li, Xuelong1; Cui, Guosheng1,2; Dong, Yongsheng1,3; Dong, Yongsheng (dongyongsheng98@163.com) | |
刊名 | ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY |
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 |
ISSN号 | 2157-6904 |
DOI | 10.1145/3090312 |
产权排序 | 1 |
文献子类 | 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关键词 | NONLINEAR DIMENSIONALITY REDUCTION ; GEOMETRIC FRAMEWORK ; REPRESENTATION |
WOS研究方向 | Computer Science |
语种 | 英语 |
WOS记录号 | WOS:000414316900001 |
资助机构 | 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) |
内容类型 | 期刊论文 |
源URL | [http://ir.opt.ac.cn/handle/181661/29251] |
专题 | 西安光学精密机械研究所_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 |
推荐引用方式 GB/T 7714 | 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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