Uncertainty in Artificial Intelligence
First Name   Last Name   Password   Forgot Password   Log in!
    Proceedings   Proceeding details   Article details         Authors         Search    
Dirichlet Process Mixtures of Generalized Mallows Models
Marina Meila, Harr Chen
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 realworld ranking datasets.
Keywords:
Pages: 358-367
PS Link:
PDF Link: /papers/10/p358-meila.pdf
BibTex:
@INPROCEEDINGS{Meila10,
AUTHOR = "Marina Meila and Harr Chen",
TITLE = "Dirichlet Process Mixtures of Generalized Mallows Models",
BOOKTITLE = "Proceedings of the Twenty-Sixth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-10)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2010",
PAGES = "358--367"
}


hosted by DSL   •   site info   •   help