Uncertainty in Artificial Intelligence
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Pitman-Yor Diffusion Trees
David Knowles, Zoubin Ghahramani
Abstract:
We introduce the Pitman Yor Diffusion Tree (PYDT) for hierarchical clustering, a generalization of the Dirichlet Diffusion Tree (Neal, 2001) which removes the restriction to binary branching structure. The generative process is described and shown to result in an exchangeable distribution over data points. We prove some theoretical properties of the model and then present two inference methods: a collapsed MCMC sampler which allows us to model uncertainty over tree structures, and a computationally efficient greedy Bayesian EM search algorithm. Both algorithms use message passing on the tree structure. The utility of the model and algorithms is demonstrated on synthetic and real world data, both continuous and binary.
Keywords:
Pages: 410-418
PS Link:
PDF Link: /papers/11/p410-knowles.pdf
BibTex:
@INPROCEEDINGS{Knowles11,
AUTHOR = "David Knowles and Zoubin Ghahramani",
TITLE = "Pitman-Yor Diffusion Trees",
BOOKTITLE = "Proceedings of the Twenty-Seventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-11)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2011",
PAGES = "410--418"
}


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