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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: 2017-08
卷号: 9, 期号:1
DOI: 10.1145/3090312
通讯作者: Dong, Yongsheng (dongyongsheng98@163.com)
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 from the 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 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. © 2017 ACM.

收录类别: EI
语种: 英语
ISSN号: 21576904
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.Center for OPTical IMagery Analysis and Learning (OPTIMAL), State Key Laboratory of Transient Optics and Photonics, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, Shaanxi; 710119, China
2.University of Chinese, Academy of Sciences, 19A Yuquanlu, Beijing; 100049, China
3.School of Information Engineering, Henan University of Science and Technology, Luoyang, Henan; 471023, China

Recommended Citation:
Li, Xuelong,Cui, Guosheng,Dong, Yongsheng. Refined-graph regularization-based Nonnegative matrix factorization[J]. ACM Transactions on Intelligent Systems and Technology,2017-08-01,9(1).
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