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Clustering-Guided Sparse Structural Learning for Unsupervised Feature Selection

文献类型：期刊论文

作者Li, Zechao1; Liu, Jing2; Yang, Yi3; Zhou, Xiaofang3; Lu, Hanqing2
刊名IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
出版日期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
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.

入库方式： OAI收割

来源：自动化研究所

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