Graph Regularized Non-Negative Low-Rank Matrix Factorization for Image Clustering | |
Li, Xuelong1; Cui, Guosheng1; Dong, Yongsheng1,2 | |
作者部门 | 光学影像学习与分析中心 |
2017-11-01 | |
发表期刊 | IEEE TRANSACTIONS ON CYBERNETICS
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ISSN | 2168-2267 |
卷号 | 47期号:11页码:3840-3853 |
产权排序 | 1 |
摘要 | Non-negative matrix factorization (NMF) has been one of the most popular methods for feature learning in the field of machine learning and computer vision. Most existing works directly apply NMF on high-dimensional image datasets for computing the effective representation of the raw images. However, in fact, the common essential information of a given class of images is hidden in their low rank parts. For obtaining an effective low-rank data representation, we in this paper propose a non-negative low-rank matrix factorization (NLMF) method for image clustering. For the purpose of improving its robustness for the data in a manifold structure, we further propose a graph regularized NLMF by incorporating the manifold structure information into our proposed objective function. Finally, we develop an efficient alternating iterative algorithm to learn the low-dimensional representation of low-rank parts of images for clustering. Alternatively, we also incorporate robust principal component analysis into our proposed scheme. Experimental results on four image datasets reveal that our proposed methods outperform four representative methods. |
文章类型 | Article |
关键词 | Data Representation Graph Regularization Image Clustering Low-rank Recovery Non-negative Matrix Factorization (Nmf) |
WOS标题词 | Science & Technology ; Technology |
DOI | 10.1109/TCYB.2016.2585355 |
收录类别 | SCI ; EI |
关键词[WOS] | REPRESENTATION ; RECOGNITION ; CLASSIFICATION ; INFORMATION ; RETRIEVAL ; PARTS ; SEGMENTATION ; OBJECTS ; SPARSE ; RULES |
语种 | 英语 |
WOS研究方向 | Computer Science |
项目资助者 | National Natural Science Foundation of China(61125106 ; International Science and Technology Cooperation Project of Henan Province(162102410021) ; China Post-Doctoral Science Foundation(2014M550517 ; Chinese Academy of Sciences(KGZD-EW-T03) ; 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) ; 61301230) ; 2015T81063) |
WOS类目 | Computer Science, Artificial Intelligence ; Computer Science, Cybernetics |
WOS记录号 | WOS:000413003100029 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.opt.ac.cn/handle/181661/29371 |
专题 | 光谱成像技术研究室 |
作者单位 | 1.Chinese Acad Sci, Ctr Opt Imagery Anal & Learning, State Key Lab Transient Opt & Photon, Xian Inst Opt & Precis Mech, Xian 710119, Shaanxi, Peoples R China 2.Henan Univ Sci & Technol, Informat Engn Coll, Luoyang 471023, Peoples R China |
推荐引用方式 GB/T 7714 | Li, Xuelong,Cui, Guosheng,Dong, Yongsheng. Graph Regularized Non-Negative Low-Rank Matrix Factorization for Image Clustering[J]. IEEE TRANSACTIONS ON CYBERNETICS,2017,47(11):3840-3853. |
APA | Li, Xuelong,Cui, Guosheng,&Dong, Yongsheng.(2017).Graph Regularized Non-Negative Low-Rank Matrix Factorization for Image Clustering.IEEE TRANSACTIONS ON CYBERNETICS,47(11),3840-3853. |
MLA | Li, Xuelong,et al."Graph Regularized Non-Negative Low-Rank Matrix Factorization for Image Clustering".IEEE TRANSACTIONS ON CYBERNETICS 47.11(2017):3840-3853. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Graph Regularized No(1643KB) | 期刊论文 | 出版稿 | 限制开放 | CC BY-NC-SA | 请求全文 |
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