Parallel Sampling of HDPs using Sub-Cluster Splits

Jason Chang, John W. Fisher III
2014 Neural Information Processing Systems  
We develop a sampling technique for Hierarchical Dirichlet process models. The parallel algorithm builds upon [1] by proposing large split and merge moves based on learned sub-clusters. The additional global split and merge moves drastically improve convergence in the experimental results. Furthermore, we discover that cross-validation techniques do not adequately determine convergence, and that previous sampling methods converge slower than were previously expected.
dblp:conf/nips/ChangF14 fatcat:625uoui5p5g7nhguhxebu5e344