# 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