Parallel Sampling of HDPs using Sub-Cluster Splits

Part of Advances in Neural Information Processing Systems 27 (NIPS 2014)

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Jason Chang, John W. Fisher III


We develop a sampling technique for Hierarchical Dirichlet process models. The parallel algorithm builds upon [Chang & Fisher 2013] 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.