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Local Regression and Global Information-Embedded Dimension Reduction
Yao, Chao1; Han, Junwei1; Nie, Feiping2; Xiao, Fu3; Li, Xuelong4
作者部门光学影像学习与分析中心
2018-10
发表期刊IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN2162-237X;2162-2388
卷号29期号:10页码:4882-4893
产权排序4
摘要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.
关键词Dimension Reduction Feature Extraction Manifold Learning Unsupervised Learning
DOI10.1109/TNNLS.2017.2783384
收录类别SCI
语种英语
WOS记录号WOS:000445351300027
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型期刊论文
条目标识符http://ir.opt.ac.cn/handle/181661/30641
专题光学影像学习与分析中心
通讯作者Nie, Feiping
作者单位1.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
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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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