Choosing the Number of Topics in LDA Models – A Monte Carlo Comparison of Selection Criteria

Selecting the number of topics in Latent Dirichlet Allocation (LDA) models is considered to be a difficult task, for which various approaches have been proposed. In this paper the performance of the recently developed singular Bayesian information criterion (sBIC) is evaluated and compared to the performance of alternative model selection criteria. The sBIC is a generalization of the standard BIC that can be applied to singular statistical models. The comparison is based on Monte Carlo simulations and carried out for …

bayesian comparison criterion information lda paper performance singular topics