摘要
本文讨论了一种基于正交变换的文本特征降维方法。分析了基于特征选择和特征抽取的特征降维方法各自特点,借助矩阵的分解论证了基于Fisher准则函数的特征降维模式的原理与理论基础,讨论了PCA与SVD两种模式的相互关系。实验结果表明这种特征降维模式在文本分类的准确性方面效果较好。
Discuss a method of reducing the text features based on orthogonal transforming. Then, we analyze the characteristic about feature selection and feature extraction. In virtue of decomposing the matrix, we demonstrate the principle and the theory foundation about the reduce of dimensions based on Fisher. We discuss the relation of PCA and SVD. The test shows that this method has a better precision in the text categorization.
出处
《计算机科学》
CSCD
北大核心
2008年第5期125-126,162,共3页
Computer Science
基金
国家自然科学基金(编号:70571087)
关键词
文本分类
特征抽取
特征降维
正交变换
奇异值分解
Text classification, Feature extraction, Feature selection, Orthogonal transforming, SVD