基于局部判别约束的半监督特征选择方法<sup>*</sup>

doi:10.16451/j.cnki.issn1003-6059.201701010

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Abstract In feature selection the most representative features are selected and processed to reduce the dimensionality of feature space. A local discriminant constraint based semi-supervised feature selection method is presented in this paper. The labeled and unlabeled training samples are completely utilized to construct feature selection model, and the local discriminant information between the adjacent data is adopted to improve model accuracy. Then the l_2,1 constraint is added to improve the distinguishability between these features and avoid noise interference. Finally, several state-of-the-art feature selection methods are performed to compare with the proposed algorithm. The experimental results demonstrate the effectiveness of the proposed algorithm.

Key words： Feature Selection Semi supervised Learning Local Discriminant Constraint l₂₁Norm

Received: 16 September 2016

ZTFLH:

TP 391.4

Fund:Supported by Natural Science Foundation of Fujian Province(No.2016J01324), Scientific Research Fund of Fujian Provincial Education Department(No.JA15385), International Science and Technology Cooperation Program of Xiamen University of Technology(No.E201400400)

About author:: YAN Fei(Corresponding author), born in 1985, master, experimentalist. Her research interests include pattern recognition and data hiding. WANG Xiaodong, born in 1983, master, lecturer. His research interests include pattern recognition and image processing.

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YAN Fei,WANG Xiaodong. A Semi-supervised Feature Selection Method Based on Local Discriminant Constraint[J]. , 2017, 30(1): 89-95.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201701010 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2017/V30/I1/89

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