Uncertainty in Artificial Intelligence
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Nonparametric Clustering with Distance Dependent Hierarchies
Soumya Ghosh, Michalis Raptis, Leonid Sigal, Erik Sudderth
The distance dependent Chinese restaurant pro- cess (ddCRP) provides a flexible framework for clustering data with temporal, spatial, or other structured dependencies. Here we model mul- tiple groups of structured data, such as pixels within frames of a video sequence, or paragraphs within documents from a text corpus. We pro- pose a hierarchical generalization of the ddCRP which clusters data within groups based on dis- tances between data items, and couples clusters across groups via distances based on aggregate properties of these local clusters. Our hddCRP model subsumes previously proposed hierarchi- cal extensions to the ddCRP, and allows more flexibility in modeling complex data. This flexi- bility poses a challenging inference problem, and we derive a MCMC method that makes coordi- nated changes to data assignments both within and between local clusters. We demonstrate the effectiveness of our hddCRP on video segmenta- tion and discourse modeling tasks, achieving re- sults competitive with state-of-the-art methods.
Pages: 260-269
PS Link:
PDF Link: /papers/14/p260-ghosh.pdf
AUTHOR = "Soumya Ghosh and Michalis Raptis and Leonid Sigal and Erik Sudderth",
TITLE = "Nonparametric Clustering with Distance Dependent Hierarchies",
BOOKTITLE = "Proceedings of the Thirtieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-14)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2014",
PAGES = "260--269"

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