Biased Parameter Estimation for LDA
Latent Dirichlet Allocation(LDA)and other related topic models are increasingly popular tools for applications in discrete data.However,the Dirichlet distribution over latent topics in LDA did not capture the co-relation between topics very well.To overcome the drawback,directed acyclic graph(DAG)and other algebra distribution,such as logistic normal distribution,were introduced to describe the correlations between topics.They were effective but relatively expensive.We alleviated it by bias parameter estimation,which made the topics in LDA more independent than standard LDA.We introduced the background for biased estimation at first.Then we presented the detail method of the biased estimation within LDA frame.We report the result of the biased model in Ad hoc IR experiment showing that the biased estimation outperforms the basic EM method.Finally,the influence of some model parameters was analysis briefly.