组合降维技术在中文网页分类中的应用

基于向量空间模型的文本分类中特征向量是极度稀疏的高维向量，只有降低向量空间维数才能提高分类效率。在利用统计方法选择文本分类特征降低特征空间维数的基础上，采用隐含语义分析技术，挖掘文档特征间的语义信息，利用矩阵奇异值分解理论进一步降低了特征空间维数。实验结果表明分类结果宏平均F1约提高了5%，验证了该方法的有效性。

Web page categorization based on LSA and features selection

The feature vector of Chinese Web page is high dimension and very sparse for text categorization.How to reduce the dimensionality of feature space is a very key problem for practical text classification.In this paper a new method is described.The approach is to take advantage of latent semantic analysis and feature selection that use statistical methods.The K-Nearest Neighbor method is selected as the evaluating classifiers.The experimental result shows that the proposed method for Chinese Web page categorization to be promising.