LLE Score: A New Filter-Based Unsupervised Feature Selection Method Based on Nonlinear Manifold Embedding and Its Application to Image Recognition

doi:10.1109/TIP.2017.2733200

	LLE Score: A New Filter-Based Unsupervised Feature Selection Method Based on Nonlinear Manifold Embedding and Its Application to Image Recognition
	Yao, Chao 1; Liu, Ya-Feng2 ; Jiang, Bo 3; Han, Jungong 4; Han, Junwei 1
	2017-11-01
发表期刊	IEEE TRANSACTIONS ON IMAGE PROCESSING
ISSN	1057-7149
卷号	26 期号:11 页码:5257-5269
摘要	The 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.
关键词	Unsupervised learning feature selection manifold learning image recognition
DOI	10.1109/TIP.2017.2733200
语种	英语
资助项目	China 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研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000407969200013
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.amss.ac.cn/handle/2S8OKBNM/26361
专题	计算数学与科学工程计算研究所
通讯作者	Han, Junwei
作者单位	1.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
推荐引用方式 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.