Noise reduction in a statistical approach to text categorization

Abstract

This paper studies noise reduction for computational efficiency improvements in a statistical learning method for text categorization, the Linear Least Squares Fit (LLSF) mapping. Multiple noise reduction strategies are proposedand evaluated, including: an aggressive removal of "non-informative words" from texts before training; the use of a truncated singular value decomposition to cut off noisy "latent semantic structures" during training; the elimination of non-influential components in the LLSF solution (a word-concept association matrix) after training. Text collections in different domains were used for evaluation. Significant improvements in computational efficiency without losing categorization accuracy were evident in the testing results. 1 Introduction The task of text categorization is to assign predefined categories to texts. It has wide application since a controlled vocabulary (subject categories) is often used to index real-world databases for retrieval purposes. While ...