Dirichlet Process Mixtures of Generalized Mallows Models

Marina Meila, Harr Chen
Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:365-374, 2010.

Abstract

We present a Dirichlet process mixture model over discrete incomplete rankings and study two Gibbs sampling inference techniques for estimating posterior clusterings. The first approach uses a slice sampling subcomponent for estimating cluster parameters. The second approach marginalizes out several cluster parameters by taking advantage of approximations to the conditional posteriors. We empirically demonstrate (1) the effectiveness of this approximation for improving convergence, (2) the benefits of the Dirichlet process model over alternative clustering techniques for ranked data, and (3) the applicability of the approach to exploring large real-world ranking datasets.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR8-meila10a, title = {{D}irichlet Process Mixtures of Generalized Mallows Models}, author = {Meila, Marina and Chen, Harr}, booktitle = {Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence}, pages = {365--374}, year = {2010}, editor = {Grünwald, Peter and Spirtes, Peter}, volume = {R8}, series = {Proceedings of Machine Learning Research}, month = {08--11 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r8/main/assets/meila10a/meila10a.pdf}, url = {https://proceedings.mlr.press/r8/meila10a.html}, abstract = {We present a Dirichlet process mixture model over discrete incomplete rankings and study two Gibbs sampling inference techniques for estimating posterior clusterings. The first approach uses a slice sampling subcomponent for estimating cluster parameters. The second approach marginalizes out several cluster parameters by taking advantage of approximations to the conditional posteriors. We empirically demonstrate (1) the effectiveness of this approximation for improving convergence, (2) the benefits of the Dirichlet process model over alternative clustering techniques for ranked data, and (3) the applicability of the approach to exploring large real-world ranking datasets.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Dirichlet Process Mixtures of Generalized Mallows Models %A Marina Meila %A Harr Chen %B Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2010 %E Peter Grünwald %E Peter Spirtes %F pmlr-vR8-meila10a %I PMLR %P 365--374 %U https://proceedings.mlr.press/r8/meila10a.html %V R8 %X We present a Dirichlet process mixture model over discrete incomplete rankings and study two Gibbs sampling inference techniques for estimating posterior clusterings. The first approach uses a slice sampling subcomponent for estimating cluster parameters. The second approach marginalizes out several cluster parameters by taking advantage of approximations to the conditional posteriors. We empirically demonstrate (1) the effectiveness of this approximation for improving convergence, (2) the benefits of the Dirichlet process model over alternative clustering techniques for ranked data, and (3) the applicability of the approach to exploring large real-world ranking datasets. %Z Reissued by PMLR on 04 October 2026.
APA
Meila, M. & Chen, H.. (2010). Dirichlet Process Mixtures of Generalized Mallows Models. Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R8:365-374 Available from https://proceedings.mlr.press/r8/meila10a.html. Reissued by PMLR on 04 October 2026.

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