Comparing Dimension Reduction Methods of Text Feature Matrix
Wang Zheng-ou · Computer Engineering and Applications Journal · 2006
Vector Space Model is usually used to express text feature in data mining.Text feature matrix has large dimensionality,and leads to complex computation.So it is needed to reduce dimensionality of text feature matrix before mining data.Latent Semantic Analysis,Concept Indexing,Non-negative Matrix Factorization and Random Projection are some dimension reduction methods.After comparing and analyzing the meanings of the reduced space,the computing complexity and their differences,experiments demonstrate these methods not only can reduce dimensionality effectively,but also open out the semantic relations between text and term and improve mining efficiency and accuracy.