关键词: 粒子群算法, 独立分量分析, 特征降维, 文本分类

Abstract: Text classification often utilizes Vector Space Model（VSM） to express text features, which is prone to high-dimensional and sparse eigenvectors. Aiming at simplifying the original text feature vectors effectively, a dimensionality reduction algorithm based on Independent Component Analysis（ICA） optimized by Particle Swarm Optimization（PSO） is proposed and applied to text classification. The negative entropy is adopted as the fitness function of the particle swarm optimization and its Gaussian principle is regarded as the discriminant standard of independence to adaptively update the separation matrix. Experimental results demonstrate the proposed method can address the dimensionality reduction problem of the high-dimensional text feature vector and significantly improve the efficiency and accuracy of the text classification compared to the conventional dimensionality reduction methods.

Key words: particle swarm optimization, independent component analysis, dimension reduction, text classification