Robust Adaptive Sparse Learning Method for Graph Clustering | |
Chen, Mulin1; Wang, Qi1,2; Li, Xuelong3,4 | |
2018-08-29 | |
会议日期 | 2018-10-07 |
会议地点 | Athens, Greece |
DOI | 10.1109/ICIP.2018.8451374 |
页码 | 1618-1622 |
英文摘要 | Graph clustering aims to group the data into clusters according to a similarity graph, and has received sufficient attention in computer vision. As the basis of clustering, the quality of graph affects the results directly. In this paper, a Robust Adaptive Sparse Learning (RASL) method is proposed to improve the graph quality. The contributions made in this paper are three fold: (1) the sparse representation technique is employed to enforce the graph sparsity, and the ell-2,1 norm is introduced to improve the robustness; (2) the intrinsic manifold structure is captured by investigating the local relationship of data points; (3) an efficient optimization algorithm is designed to solve the proposed problem. Experimental results on various real-world benchmark datasets demonstrate the promising results of the proposed graph-based clustering method. © 2018 IEEE. |
产权排序 | 3 |
会议录 | 2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings |
会议录出版者 | IEEE Computer Society |
语种 | 英语 |
ISSN号 | 15224880 |
ISBN号 | 9781479970612 |
内容类型 | 会议论文 |
源URL | [http://ir.opt.ac.cn/handle/181661/31345] |
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 | Wang, Qi |
作者单位 | 1.School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi'an Shaanxi; 710072, China; 2.Unmanned System Research Institute (USRI), Northwestern Polytechnical University, Xi'an Shaanxi; 710072, China; 3.Xi'An Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an Shaanxi; 710119, China; 4.University of Chinese Academy of Sciences, Beijing; 100049, China |
推荐引用方式 GB/T 7714 | Chen, Mulin,Wang, Qi,Li, Xuelong. Robust Adaptive Sparse Learning Method for Graph Clustering[C]. 见:. Athens, Greece. 2018-10-07. |
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