Support Vector Machines for Text Categorization Based on Latent Semantic Indexing

Text Categorization(TC) is an important component in many information organization and information management tasks. Two key issues in TC are feature coding and classifier design. In this paper Text Categorization via Support Vector Machines(SVMs) approach based on Latent Semantic Indexing(LSI) is described. Latent Semantic Indexing[1][2] is a method for selecting informative subspaces of feature spaces with the goal of obtaining a compact representation of document. Support Vector Machines[3] are powerful machine learning systems, which combine remarkable performance with an elegant theoretical framework. The SVMs well fits the Text Categorization task due to the special properties of text itself. Experiments show that the LSI+SVMs frame improves clustering performance by focusing attention of Support Vector Machines onto informative subspaces of the feature spaces.