Clustering-Guided Sparse Structural Learning for Unsupervised Feature Selection

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	Clustering-Guided Sparse Structural Learning for Unsupervised Feature Selection
	Li, Zechao 1; Liu, Jing2 ; Yang, Yi 3; Zhou, Xiaofang 3; Lu, Hanqing2
刊名	IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
	2014-09-01
卷号	26 期号:9 页码:2138-2150
关键词	Feature selection nonnegative spectral clustering latent structure row-sparsity
英文摘要	Many pattern analysis and data mining problems have witnessed high-dimensional data represented by a large number of features, which are often redundant and noisy. Feature selection is one main technique for dimensionality reduction that involves identifying a subset of the most useful features. In this paper, a novel unsupervised feature selection algorithm, named clustering-guided sparse structural learning (CGSSL), is proposed by integrating cluster analysis and sparse structural analysis into a joint framework and experimentally evaluated. Nonnegative spectral clustering is developed to learn more accurate cluster labels of the input samples, which guide feature selection simultaneously. Meanwhile, the cluster labels are also predicted by exploiting the hidden structure shared by different features, which can uncover feature correlations to make the results more reliable. Row-wise sparse models are leveraged to make the proposed model suitable for feature selection. To optimize the proposed formulation, we propose an efficient iterative algorithm. Finally, extensive experiments are conducted on 12 diverse benchmarks, including face data, handwritten digit data, document data, and biomedical data. The encouraging experimental results in comparison with several representative algorithms and the theoretical analysis demonstrate the efficiency and effectiveness of the proposed algorithm for feature selection.
WOS标题词	Science & Technology ; Technology
类目[WOS]	Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic
研究领域[WOS]	Computer Science ; Engineering
关键词[WOS]	IMAGE ANNOTATION ; CLASSIFICATION ; FRAMEWORK ; DESIGN ; MODELS
收录类别	SCI
语种	英语
WOS记录号	WOS:000341571100005
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/3346]
专题	自动化研究所_模式识别国家重点实验室_图像与视频分析团队
作者单位	1.Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Jiangsu, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 3.Univ Queensland, Sch Informat Technol & Elect Engn, Brisbane, Qld 4072, Australia
推荐引用方式 GB/T 7714	Li, Zechao,Liu, Jing,Yang, Yi,et al. Clustering-Guided Sparse Structural Learning for Unsupervised Feature Selection[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2014,26(9):2138-2150.
APA	Li, Zechao,Liu, Jing,Yang, Yi,Zhou, Xiaofang,&Lu, Hanqing.(2014).Clustering-Guided Sparse Structural Learning for Unsupervised Feature Selection.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,26(9),2138-2150.
MLA	Li, Zechao,et al."Clustering-Guided Sparse Structural Learning for Unsupervised Feature Selection".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 26.9(2014):2138-2150.