Dependent hierarchical normalized random measures for dynamic topic modeling
We develop dependent hierarchical normalized random measures and apply them to dynamic topic modeling. The dependency arises via superposition, subsampling and point transition on the underlying Poisson processes of these measures. The measures used include normalised generalised Gamma processes that demonstrate power law properties, unlike Dirichlet processes used previously in dynamic topic modeling. Inference for the model includes adapting a recently developed slice sampler to directly...[Show more]
|Collections||ANU Research Publications|
|Source:||Proceedings of the 29th International Conference on Machine Learning, ICML 2012|
|01_Chen_Dependent_hierarchical_2012.pdf||455.37 kB||Adobe PDF||Request a copy|
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