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Local Regression and Global Information-Embedded Dimension Reduction
Yao, Chao1; Han, Junwei1; Nie, Feiping2; Xiao, Fu3; Li, Xuelong4
Department光学影像学习与分析中心
2018-10
Source PublicationIEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN2162-237X;2162-2388
Volume29Issue:10Pages:4882-4893
Contribution Rank4
Abstract

A large family of algorithms for unsupervised dimension reduction is based on both the local and global structures of the data. A fundamental step in these methods is to model the local geometrical structure of the data. However, the previous methods mainly ignore two facts in this step: 1) the dimensionality of the data is usually far larger than the number of local data, which is a typical ill-posed problem and 2) the data might be polluted by noise. These facts normally may lead to an inaccurate learned local structure and may degrade the final performance. In this paper, we propose a novel unsupervised dimension reduction method with the ability to address these problems effectively while also preserving the global information of the input data. Specifically, we first denoise the local data by preserving their principal components and we then apply a regularization term to the local modeling function to solve the illposed problem. Then, we use a linear regression model to capture the local geometrical structure, which is demonstrated to be insensitive to the parameters. Finally, we propose two criteria to simultaneously model both the local and the global information. Theoretical analyses for the relations between the proposed methods and some classical dimension-reduction methods are presented. The experimental results from various databases demonstrate the effectiveness of our methods.

KeywordDimension Reduction Feature Extraction Manifold Learning Unsupervised Learning
DOI10.1109/TNNLS.2017.2783384
Indexed BySCI ; EI
Language英语
WOS IDWOS:000445351300027
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
EI Accession Number20180304658622
Citation statistics
Cited Times:3[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.opt.ac.cn/handle/181661/30641
Collection光学影像学习与分析中心
Corresponding AuthorNie, Feiping
Affiliation1.Northwestern Polytech Univ, Sch Automat, Xian 710072, Peoples R China
2.Northwestern Polytech Univ, Sch Comp Sci, Ctr OPT IMagery Anal & Learning, Xian 710072, Peoples R China
3.Nanjing Univ Posts & Telecommun, Coll Comp, Nanjing 210046, Jiangsu, Peoples R China
4.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Ctr OPT IMagery Anal & Learning, State Key Lab Transient Opt & Photon, Xian 710119, Shaanxi, Peoples R China
Recommended Citation
GB/T 7714
Yao, Chao,Han, Junwei,Nie, Feiping,et al. Local Regression and Global Information-Embedded Dimension Reduction[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2018,29(10):4882-4893.
APA Yao, Chao,Han, Junwei,Nie, Feiping,Xiao, Fu,&Li, Xuelong.(2018).Local Regression and Global Information-Embedded Dimension Reduction.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,29(10),4882-4893.
MLA Yao, Chao,et al."Local Regression and Global Information-Embedded Dimension Reduction".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 29.10(2018):4882-4893.
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