其基本思想是：对于文本特征向量进行概念扩充，减少特征项之间的相关性，增强特征项的表现能力。
It applies the concept and association expansion to text feature vectors in order to reduce the relevant degree of terms and enhance the ability to represent the text theme.
通过将对分类有相同贡献的文本特征词聚合，使用它们共同的分类贡献向量特征模式作为文本特征向量的基本维；
Multiple discriminating features with similar contribution to classification are combined into one pattern, which is used as the basic feature dimension.
文本分类中特征向量空间是高维和稀疏的，降维处理是分类的关键步骤。
Feature space is high dimensional and sparse in text categorization, the process of dimension reduction is a very key problem for large-scale text categorization.
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