Electrical Engineering
      and Computer Sciences

Electrical Engineering and Computer Sciences

COLLEGE OF ENGINEERING

UC Berkeley

Graph Partition Strategies for Generalized Mean Field Inference

Eric P. Xing and Michael I. Jordan

EECS Department
University of California, Berkeley
Technical Report No. UCB/CSD-03-1274
June 2003

An autonomous variational inference algorithm for arbitrary graphical model requires the ability to optimize variational approximations over the space of model parameters as well as over the choice of tractable families used for the variational approximation. In this paper, we present a novel combination of graph partitioning algorithms with a generalized mean field (GMF) inference algorithm. This combination optimizes over disjoint clustering of variables and performs inference using those clusters. We explore several graph partition strategies empirically, including several variants of MinCut and MaxCut algorithms. Our results provide rather clear support for a weighted version of MinCut as a useful clustering algorithm for generalized mean field inference.


BibTeX citation:

@techreport{Xing:CSD-03-1274,
    Author = {Xing, Eric P. and Jordan, Michael I.},
    Title = {Graph Partition Strategies for Generalized Mean Field Inference},
    Institution = {EECS Department, University of California, Berkeley},
    Year = {2003},
    Month = {Jun},
    URL = {http://www.eecs.berkeley.edu/Pubs/TechRpts/2003/6237.html},
    Number = {UCB/CSD-03-1274},
    Abstract = {An autonomous variational inference algorithm for arbitrary graphical model requires the ability to optimize variational approximations over the space of model parameters as well as over the choice of tractable families used for the variational approximation. In this paper, we present a novel combination of graph partitioning algorithms with a generalized mean field (GMF) inference algorithm. This combination optimizes over disjoint clustering of variables and performs inference using those clusters. We explore several graph partition strategies empirically, including several variants of MinCut and MaxCut algorithms. Our results provide rather clear support for a weighted version of MinCut as a useful clustering algorithm for generalized mean field inference.}
}

EndNote citation:

%0 Report
%A Xing, Eric P.
%A Jordan, Michael I.
%T Graph Partition Strategies for Generalized Mean Field Inference
%I EECS Department, University of California, Berkeley
%D 2003
%@ UCB/CSD-03-1274
%U http://www.eecs.berkeley.edu/Pubs/TechRpts/2003/6237.html
%F Xing:CSD-03-1274