Nonparametric Clustering with Distance Dependent Hierarchies

Soumya Ghosh Brown University, Michalis Raptis, Leonid Sigal Disney Research, Erik Sudderth Brown University
Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, PMLR R12:628-637, 2014.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR12-university14p, title = {Nonparametric Clustering with Distance Dependent Hierarchies}, author = {University, Soumya Ghosh Brown and Raptis, Michalis and Research, Leonid Sigal Disney and University, Erik Sudderth Brown}, booktitle = {Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence}, pages = {628--637}, year = {2014}, editor = {Zhang, Nevin L. and Tian, Jin}, volume = {R12}, series = {Proceedings of Machine Learning Research}, month = {23--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r12/main/assets/university14p/university14p.pdf}, url = {https://proceedings.mlr.press/r12/university14p.html}, abstract = {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.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Nonparametric Clustering with Distance Dependent Hierarchies %A Soumya Ghosh Brown University %A Michalis Raptis %A Leonid Sigal Disney Research %A Erik Sudderth Brown University %B Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2014 %E Nevin L. Zhang %E Jin Tian %F pmlr-vR12-university14p %I PMLR %P 628--637 %U https://proceedings.mlr.press/r12/university14p.html %V R12 %X 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. %Z Reissued by PMLR on 04 October 2026.
APA
University, S.G.B., Raptis, M., Research, L.S.D. & University, E.S.B.. (2014). Nonparametric Clustering with Distance Dependent Hierarchies. Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R12:628-637 Available from https://proceedings.mlr.press/r12/university14p.html. Reissued by PMLR on 04 October 2026.

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