关键词: 文本聚类, 概率潜在语义分析, 参数初始化, 潜在语义分析

Abstract: Trained by the Expectation Maximization (EM) algorithm, whose model parameters are randomly initialized, the performance of Probabilistic Latent Semantic Analysis (PLSA) model is quite dependent on the initialization of the model, and the result of iteration is not a global maximum, but a local one. The authors derived probabilities from Latent Semantic Analysis (LSA), and then used it to initialize the parameters of PLSA model in documents clustering. The improved PLSA could effectively solve the puzzle of random initializing of EM. It is shown that the improved algorithm has a distinct improvement in Normalized Mutual Information (NMI) and accuracy.

Key words: document clustering, Probabilistic Latent Semantic Analysis (PLSA), model parameters initialization, Latent Semantic Analysis (LSA)