CASIA OpenIR  > 模式识别国家重点实验室  > 图像与视频分析
Clustering-Guided Sparse Structural Learning for Unsupervised Feature Selection
Li, Zechao1; Liu, Jing2; Yang, Yi3; Zhou, Xiaofang3; Lu, Hanqing2
Source PublicationIEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
2014-09-01
Volume26Issue:9Pages:2138-2150
SubtypeArticle
AbstractMany 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.
KeywordFeature Selection Nonnegative Spectral Clustering Latent Structure Row-sparsity
WOS HeadingsScience & Technology ; Technology
WOS KeywordIMAGE ANNOTATION ; CLASSIFICATION ; FRAMEWORK ; DESIGN ; MODELS
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic
WOS IDWOS:000341571100005
Citation statistics
Cited Times:158[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/3346
Collection模式识别国家重点实验室_图像与视频分析
Affiliation1.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
Recommended Citation
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.
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