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.%特征选择旨在选择待处理数据中最具代表性的特征,降低特征空间的维度.文中提出基于局部判别约束的半监督特征选择方法,充分利用已标记样本和未标记样本训练特征选择模型,并借助相邻数据间的局部判别信息提高模型的准确度,引入l2,1约束提高特征之间可区分度,避免噪声干扰.最后通过实验验证文中方法的有效性.
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