PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A kernel method for unsupervised structured network inference
Christoph Lippert, Oliver Stegle, Zoubin Ghahramani and Karsten Borgwardt
AISTATS 2009 2008.

Abstract

Network inference is the problem of inferring edges between a set of real-world objects, for instance, interactions between pairs of proteins in bioinformatics. Current kernel-based approaches to this problem all share a set of common features: (i) they are supervised and hence require labeled training data; (ii) edges in the network are treated as mutually independent and hence topological properties are largely ignored; (iii) they lack a statistical interpretation. We argue that these common assumptions are often undesirable for network inference, and propose (i) an unsupervised kernel method (ii) that takes the global structure of the network into account and (iii) is statistically motivated. We show that our approach can explain commonly used heuristics in statistical terms. In experiments on social networks and unsupervised protein interaction prediction we compare different variants of our method which demonstrate appealing predictive performance.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:4963
Deposited By:Karsten Borgwardt
Deposited On:24 March 2009