Adaptive Projected Matrix Factorization method for data clustering | |
Chen, Mulin1,2; Wang, Qi1,2,3; Li, Xuelong4,5; Wang, Qi (crabwq@gmail.com) | |
刊名 | NEUROCOMPUTING |
2018-09-06 | |
卷号 | 306页码:182-188 |
关键词 | Clustering Graph Learning Subspace Learning Matrix Factorization |
ISSN号 | 0925-2312 |
DOI | 10.1016/j.neucom.2018.04.031 |
产权排序 | 3 |
文献子类 | Article |
英文摘要 | Data clustering aims to group the data samples into clusters, and has attracted many researchers in a variety of multidisciplinary fields, such as machine learning and data mining. In order to capture the geometry structure, many methods perform clustering according to a predefined affinity graph. So the clustering performance is largely determined by the graph quality. Unfortunately, the graph quality cannot be guaranteed in various real-world applications. In this paper, an Adaptive Projected Matrix Factorization (APMF) method is proposed for data clustering. Our contributions are threefold: (1) instead of keeping the graph fixed, graph learning is taken as a part of the clustering procedure; (2) the clustering is performed in the projected subspace, so the noise in the input data space is alleviated; (3) an efficient and effective algorithm is developed to solve the proposed problem, and its convergence is proved. Extend experiments on nine real-world benchmarks validate the effectiveness of the proposed method, and verify its superiority against the state-of-the-art competitors. (C) 2018 Elsevier B.V. All rights reserved. |
WOS关键词 | Representation |
WOS研究方向 | Computer Science |
语种 | 英语 |
WOS记录号 | WOS:000433212700015 |
资助机构 | National Key R&D Program of China(2017YFB1002202) ; National Natural Science Foundation of China(61773316) ; Fundamental Research Funds for the Central Universities(3102017AX010) ; Open Research Fund of Key Laboratory of Spectral Imaging Technology, Chinese Academy of Sciences |
内容类型 | 期刊论文 |
源URL | [http://ir.opt.ac.cn/handle/181661/30170] |
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 (crabwq@gmail.com) |
作者单位 | 1.Northwestern Polytech Univ, Sch Comp Sci, Xian 710072, Shaanxi, Peoples R China 2.Northwestern Polytech Univ, Ctr OPT Imagery Anal & Learning OPTIMAL, Xian 710072, Shaanxi, Peoples R China 3.Northwestern Polytech Univ, USRI, Xian 710072, Shaanxi, Peoples R China 4.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian 710119, Shaanxi, Peoples R China 5.Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
推荐引用方式 GB/T 7714 | Chen, Mulin,Wang, Qi,Li, Xuelong,et al. Adaptive Projected Matrix Factorization method for data clustering[J]. NEUROCOMPUTING,2018,306:182-188. |
APA | Chen, Mulin,Wang, Qi,Li, Xuelong,&Wang, Qi .(2018).Adaptive Projected Matrix Factorization method for data clustering.NEUROCOMPUTING,306,182-188. |
MLA | Chen, Mulin,et al."Adaptive Projected Matrix Factorization method for data clustering".NEUROCOMPUTING 306(2018):182-188. |
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