中文摘要：

为降低特征空间维数，提出了一种基于分布距离的文本特征聚类方法，通过将特征空间中分布距离相近的特征聚合，来实现降维。在TanCorpusV1．0语料库上实验表明，当将特征空间维数降低至原空间的近10％时，用SVM作为分类器，获得了比特征提取方法高的分类精度。

英文摘要：

To reduce feature space dimensionality,this paper presents a new method to cluster the similar features based on distribution distance, which can achieve dimensionality reduction through clustering the nearest distance features.Test on the corpus of TanCorpusVl.O shows,when reducing the dimensionality of feature space as far as original＇s 10%,using SVM as classifier,this method can achieve a higher accuracy than feature selection method.