SPARSE REPRESENTATION-BASED APPROACH FOR UNSUPERVISED FEATURE SELECTION

被引:3
作者
Su, Ya-Ru [1 ]
Li, Chuan-Xi [2 ]
Wang, Ru-Jing [3 ,4 ]
Chen, Peng [4 ]
机构
[1] Dept Fujian Prov Publ Secur, Forens Sci Div, Fuzhou 361003, Peoples R China
[2] Chinese Acad Sci, Natl Sci Lib, Beijing 100190, Peoples R China
[3] Univ Sci & Technol China, Sch Informat Sci & Technol, Hefei, Anhui, Peoples R China
[4] Chinese Acad Sci, Inst Intelligent Machines, Hefei 230027, Anhui, Peoples R China
基金
中国国家自然科学基金;
关键词
Unsupervised; feature selection; sparse representation; MOTION SEGMENTATION; SHRINKAGE; ALGORITHM;
D O I
10.1142/S0218001414500062
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Dimension reduction methods including feature selection and feature extraction have played an important role in data mining and pattern recognition. In this study, we propose a novel unsupervised feature selection approach based on sparse representation theory, namely Sparsity Score (SS). Due to the sparse representation procedure, SS not only owns the global property of Variance Score (VS) and the local property of Laplacian Score (LS), but also possesses the discriminating nature. Experimental results, based on three well-known face datasets (Yale, ORL and CMU PIE), reveal that SS performs well in the evaluation of the feature significance, and it significantly outperforms VS and LS.
引用
收藏
页数:19
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