Chinese text categorization based on CCIPCA and SMO

Vector space model is usually used to express text for text categorization. How to reduce the dimensionality of feature space is a very key problem for practical text classification. The classical decomposition algorithms are incapable of dealing with the high-dimensional and large-scale text categorization problems. In this paper an approach to improving the performance of text categorization is presented by using candid incremental principal component analysis and sequential minimization optimization algorithm. The experimental result shows that the proposed method for Chinese text categorization is practicable and effective.