We present the time-dependent topic-cluster model, a hierarchical approach for combining Latent Dirichlet Allocation and clustering via the Recurrent Chinese Restaurant Process. It inherits the advantages of both of its constituents, namely interpretability and concise representation. We show how it can be applied to streaming collections of objects such as real world feeds in a news portal. We provide details of a parallel Sequential Monte Carlo algorithm to perform inference in the resulting graphical model which scales to hundred of thousands of documents.
['Amr Ahmed', 'Qirong Ho', 'Choon Hui Teo', 'Jacob Eisenstein', 'Alexander J. Smola', 'Eric P. Xing']
Online Inference for the Infinite Topic-Cluster Model: Storylines from Streaming Text