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Adaptive Projected Matrix Factorization method for data clustering
Chen, Mulin1,2; Wang, Qi1,2,3; Li, Xuelong4,5; Wang, Qi (crabwq@gmail.com)
作者部门光学影像学习与分析中心
2018-09-06
发表期刊NEUROCOMPUTING
ISSN0925-2312
卷号306页码:182-188
产权排序3
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.

文章类型Article
关键词Clustering Graph Learning Subspace Learning Matrix Factorization
WOS标题词Science & Technology ; Technology
DOI10.1016/j.neucom.2018.04.031
收录类别SCI ; EI
关键词[WOS]REPRESENTATION
语种英语
WOS研究方向Computer Science
项目资助者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
WOS类目Computer Science, Artificial Intelligence
WOS记录号WOS:000433212700015
引用统计
文献类型期刊论文
条目标识符http://ir.opt.ac.cn/handle/181661/30170
专题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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