Bayesian learning for latent semantic analysis

Probabilistic latent semantic analysis (PLSA) is a popular approach to text modeling where the semantics and statistics in documents can be effectively captured. In this paper, a novel Bayesian PLSA framework is presented. We focus on exploiting the incremental learning algorithm for solving the updating problem of new domain articles. This algorithm is developed to improve text modeling by incrementally extracting the up-to-date latent semantic information to match the changing domains at run time. The expectation-maximization (EM) algorithm is applied to resolve the quasi-Bayes (QB) estimate of PLSA parameters. The online PLSA is constructed to accomplish parameter estimation as well as hyperparameter updating. Compared to standard PLSA using maximum likelihood estimate, the proposed QB approach is capable of performing dynamic document indexing and classification. Also, we present the maximum a posteriori PLSA for corrective training. Experiments on evaluating model perplexities and classification accuracies demonstrate the superiority of using Bayesian PLSA.