基于潜在狄利克雷分配模型和互信息的无监督特征选取法

为解决互信息(MI)在特征选取中的类别缺失和倾向低频词问题，提出 LDA-σ方法。该方法使用潜在狄利克雷分配模型(LDA)提取潜在主题，以“词—主题”间互信息的标准差作为特征评估函数。在Reuters-21578语料集上提取特征词并进行分类，LDA-σ方法的微平均F1最高达0.9096；宏平均F1优于其他算法，最高达0.7823。实验表明，LDA-σ方法可用于文本特征选取。

Unsupervised feature selection method based on latent Dirichlet allocation model and mutual information

To solve the category-deficiency and the tendency of selecting low-frequency words in feature selection process based on Mutual Information(MI),the method named LDA-σ was presented.Firstly,the latent topics were extracted by the Latent Dirichlet Allocation(LDA) model,and then the standard deviation of "Word-Topic" MI was calculated as the feature evaluation function.When conducting feature selection and categorization in Reuters-21578,the micro average F1 of LDA-σ reached up to 0.909 6,and the highest macro average F1 of LDA-σ was 0.782 3,which were higher than that of other algorithms.The experimental results indicate that LDA-σ can be applied to feature selection in text sets.