Supervised learning of a generative model for edge-weighted graphs

This paper addresses the problem of learning archetypal structural models from examples. To this end we define a generative model for graphs where the distribution of observed nodes and edges is governed by a set of independent Bernoulli trials with parameters to be estimated from data in a situation where the correspondences between the nodes in the data graphs and the nodes in the model are not not known ab initio and must be estimated from local structure. This results in an EM-like approach where we alternate the estimation of the node correspondences with the estimation of the model parameters. Parameter estimation and model order selection is addressed within a Minimum Message Length (MML) framework.

• Type: Conference
• Year: 2008
• Where: ICPR
• Authors: Andrea Torsello, David L. Dowe
• Keywords: Computer Vision, ICPR 2008, Model Order Selection, Observed Nodes, Parameter Estimation