Clique Matrices for Statistical Graph Decomposition and Parameterising Restricted Positive Definite Matrices

We introduce Clique Matrices as an alternative representation of undirected graphs, being a generalisation of the incidence matrix representation. Here we use clique matrices to decompose a graph into a set of possibly overlapping clusters, defined as wellconnected subsets of vertices. The decomposition is based on a statistical description which encourages clusters to be well connected and few in number. Inference is carried out using a variational approximation. Clique matrices also play a natural role in parameterising positive definite matrices under zero constraints on elements of the matrix. We show that clique matrices can parameterise all positive definite matrices restricted according to a decomposable graph and form a structured Factor Analysis approximation in the non-decomposable case.

• Type: Conference
• Year: 2008
• Where: UAI
• Authors: David Barber
• Keywords: Artificial Intelligence, Clique Matrices, Incidence Matrix Representation, Positive Definite Matrices, UAI 2008