Nonparametric Document Clustering with Topic Modeling

We describe a nonparametric document clustering model leveraging the topic modeling technique. In our model, the number of clusters is assumed to be inferred from data. Our model jointly optimizing two tasks: representing each document using its topic distribution, and nonparametric clustering on this transformed topic space. The clustering is built based on Dirichlet process mixture model (DPM) and the topic modeling shares similar structure with hierarchical Dirichlet process (HDP). We employ a variational inference solution to approximate the intractable posterior distribution and adopt the EM algorithm for parameter learning. Experiments on a variety of datasets are conducted to justify the effectiveness of the model.