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.
严菲,王晓栋. 基于局部判别约束的半监督特征选择方法*[J]. 模式识别与人工智能, 2017, 30(1): 89-95.
YAN Fei, WANG Xiaodong. A Semi-supervised Feature Selection Method Based on Local Discriminant Constraint. , 2017, 30(1): 89-95.
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