Text Categorization Based On Concept Indexing and Principal Component Analysis

A major problem in text categorization is the high dimensionality of feature vector space, which is about ten thousands in common. To reduce the dimensionality of the space while keeping the categorization accuracy is useful for improving categorization effectiveness and applying new categorization algorithms. Current feature selection methods for text categorization are partially effective in reducing dimensionality. We put forward a new algorithm, which combines algorithm of concept indexing and principal component analysis, for reducing dimensionality. From the experiments, we find that this algorithm can effectively reduce dimensionality without sacrificing categorization accuracy.