Dimensionality Reduction in Webpage Categorization Using Probabilistic Latent Semantic Analysis and Adaptive General Particle Swarm Optimization

A new method of text dimension reduction is brought forward based on probabilistic latent semantic analysis (PLSA) and adaptive general particle swarm optimization (AGPSO). PLSA is used to specify essential associative semantic relationships instead of the original document space. The dimension can be reduced greatly by Expectation Maximization algorithm. A crossover operator is designed to simulate the flying velocity alteration and a mutation operator is used to keep the population diversity. Besides these, an adaptive strategy is introduced to adjust probability of crossover and mutation just in order to obtain optimal feature set. The experimental results indicate that the algorithm can not only reduce dimension, but also improve categorization precision.