Parallel Sampling of HDPs Using Sub-Cluster Splits
Abstract
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.
Cite
Text
Chang and Iii. "Parallel Sampling of HDPs Using Sub-Cluster Splits." Neural Information Processing Systems, 2014.Markdown
[Chang and Iii. "Parallel Sampling of HDPs Using Sub-Cluster Splits." Neural Information Processing Systems, 2014.](https://mlanthology.org/neurips/2014/chang2014neurips-parallel/)BibTeX
@inproceedings{chang2014neurips-parallel,
title = {{Parallel Sampling of HDPs Using Sub-Cluster Splits}},
author = {Chang, Jason and Iii, John W. Fisher},
booktitle = {Neural Information Processing Systems},
year = {2014},
pages = {235-243},
url = {https://mlanthology.org/neurips/2014/chang2014neurips-parallel/}
}