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LLE Score: A New Filter-Based Unsupervised Feature Selection Method Based on Nonlinear Manifold Embedding and Its Application to Image Recognition
Yao, Chao1; Liu, Ya-Feng2; Jiang, Bo3; Han, Jungong4; Han, Junwei1
2017-11-01
Source PublicationIEEE TRANSACTIONS ON IMAGE PROCESSING
ISSN1057-7149
Volume26Issue:11Pages:5257-5269
AbstractThe task of feature selection is to find the most representative features from the original high-dimensional data. Because of the absence of the information of class labels, selecting the appropriate features in unsupervised learning scenarios is much harder than that in supervised scenarios. In this paper, we investigate the potential of locally linear embedding (LLE), which is a popular manifold learning method, in feature selection task. It is straightforward to apply the idea of LLE to the graph-preserving feature selection framework. However, we find that this straightforward application suffers from some problems. For example, it fails when the elements in the feature are all equal; it does not enjoy the property of scaling invariance and cannot capture the change of the graph efficiently. To solve these problems, we propose a new filter-based feature selection method based on LLE in this paper, which is named as LLE score. The proposed criterion measures the difference between the local structure of each feature and that of the original data. Our experiments of classification task on two face image data sets, an object image data set, and a handwriting digits data set show that LLE score outperforms state-of-the-art methods, including data variance, Laplacian score, and sparsity score.
KeywordUnsupervised learning feature selection manifold learning image recognition
DOI10.1109/TIP.2017.2733200
Language英语
Funding ProjectChina Postdoctoral Science Foundation[154906] ; Fundamental Research Funds for the Central Universities[3102016ZY022] ; Natural Science Foundation of China[61473231] ; Natural Science Foundation of China[11501298] ; Natural Science Foundation of China[11671419] ; Natural Science Foundation of China[11688101] ; NSF of Jiangsu Province[BK20150965] ; Priority Academic Program Development of Jiangsu Higher Education Institutions
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000407969200013
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/26361
Collection计算数学与科学工程计算研究所
Corresponding AuthorHan, Junwei
Affiliation1.Northwestern Polytech Univ, Sch Automat, Xian 710072, Peoples R China
2.Chinese Acad Sci, Acad Math & Syst Sci, State Key Lab Sci & Engn Comp, Inst Computat Math & Sci Engn Comp, Beijing 100190, Peoples R China
3.Nanjing Normal Univ, Sch Math Sci, Key Lab NSLSCS Jiangsu Prov, Nanjing 210023, Jiangsu, Peoples R China
4.Univ Lancaster, Sch Comp & Commun, Lancaster LA1 4YW, England
Recommended Citation
GB/T 7714
Yao, Chao,Liu, Ya-Feng,Jiang, Bo,et al. LLE Score: A New Filter-Based Unsupervised Feature Selection Method Based on Nonlinear Manifold Embedding and Its Application to Image Recognition[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2017,26(11):5257-5269.
APA Yao, Chao,Liu, Ya-Feng,Jiang, Bo,Han, Jungong,&Han, Junwei.(2017).LLE Score: A New Filter-Based Unsupervised Feature Selection Method Based on Nonlinear Manifold Embedding and Its Application to Image Recognition.IEEE TRANSACTIONS ON IMAGE PROCESSING,26(11),5257-5269.
MLA Yao, Chao,et al."LLE Score: A New Filter-Based Unsupervised Feature Selection Method Based on Nonlinear Manifold Embedding and Its Application to Image Recognition".IEEE TRANSACTIONS ON IMAGE PROCESSING 26.11(2017):5257-5269.
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