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Fast and Flexible Large Graph Embedding Based on Anchors
Yu, Weizhong1; Nie, Feiping2; Wang, Fei1; Wang, Rong2; Li, Xuelong3
Department光学影像学习与分析中心
2018-12
Source PublicationIEEE Journal on Selected Topics in Signal Processing
ISSN19324553
Volume12Issue:6Pages:1465-1475
Contribution Rank3
AbstractDimensionality reduction is one of the most fundamental topic in machine learning. A range of methods focus on dimensionality reduction have been proposed in various areas. Among the unsupervised dimensionality reduction methods, graph-based dimensionality reduction has begun to draw more and more attention due to its effectiveness. However, most existing graph-based methods have high computation complexity, which is not applicable to large-scale problems. To solve this problem, an unsupervised graph-based dimensionality reduction method called fast and flexible large graph embedding (FFLGE) based on anchors is proposed. FFLGE uses an anchor-based strategy to construct an anchor-based graph and design similarity matrix and then perform the dimensionality reduction efficiently. The computational complexity of the proposed FFLGE reduces to O(ndm), where n is the number of samples, d is the number of dimensions and m is the number of anchors. Furthermore, it is interesting to note that locality preserving projection and principal component analysis are two special cases of FFLGE. In the end, the experiments based on several publicly large-scale datasets proves the effectiveness and efficiency of the method proposed. ? 2018 IEEE.
DOI10.1109/JSTSP.2018.2873985
Indexed ByEI
Language英语
PublisherInstitute of Electrical and Electronics Engineers Inc.
EI Accession Number20184105933128
Citation statistics
Document Type期刊论文
Identifierhttp://ir.opt.ac.cn/handle/181661/31105
Collection光学影像学习与分析中心
Corresponding AuthorNie, Feiping
Affiliation1.National Engineering Laboratory for Visual Information Processing and Applications, School of Electronics and Information Engineering, Xi'an Jiaotong University, Xi'an; 710049, China;
2.School of Computer Science, Center for Optical Imagery Analysis and Learning, Northwestern Polytechnical University, Xi'an; 710072, China;
3.Center for Optical Imagery Analysis and Learning, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an; 710119, China
Recommended Citation
GB/T 7714
Yu, Weizhong,Nie, Feiping,Wang, Fei,et al. Fast and Flexible Large Graph Embedding Based on Anchors[J]. IEEE Journal on Selected Topics in Signal Processing,2018,12(6):1465-1475.
APA Yu, Weizhong,Nie, Feiping,Wang, Fei,Wang, Rong,&Li, Xuelong.(2018).Fast and Flexible Large Graph Embedding Based on Anchors.IEEE Journal on Selected Topics in Signal Processing,12(6),1465-1475.
MLA Yu, Weizhong,et al."Fast and Flexible Large Graph Embedding Based on Anchors".IEEE Journal on Selected Topics in Signal Processing 12.6(2018):1465-1475.
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