Iterative Kernel Discriminant Analysis Algorithm for Document Classification

To cope with performance and accuracy problems with high dimensionality in document classification, a novel dimensionality reduction algorithm called IKDA is proposed in this paper. The proposed IKDA algorithm combines kernel-based learning techniques and direct iterative optimization procedure to deal with the nonlinearity of the document distribution. The proposed algorithm also effectively solves the so-called "small sample size" problem in document classification task. Extensive experimental results on the real world data sets demonstrate the effectiveness and efficiency of the proposed algorithm.