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 l2,1 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.
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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