Using class-dependent projection for text categorization

Text categorization presents unique challenges to traditional classification methods due to the large number of features inherited in the datasets from real-world applications of text categorization. This paper presents a simple but effective classifier for text categorization using class-dependent projection based approach. By projecting onto a set of individual subspaces, the samples belonging to different document classes are separated such that they are easily to be classified. This is achieved by developing a supervised feature weighting algorithm to learn the optimized subspaces for each document class. The experiments carried out on common benchmarking corpus have shown that the proposed method achieves higher classification accuracy than some distinguishing classifiers in text categorization.