Dirichlet process mixtures of generalized mallows models
M Meila, H Chen - arXiv preprint arXiv:1203.3496, 2012 - arxiv.org
M Meila, H Chen
arXiv preprint arXiv:1203.3496, 2012•arxiv.orgWe 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 …
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 …
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 realworld ranking datasets.
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