Information theoretic prototype selection for unattributed graphs

Authors:
Lin Han;Luca Rossi;Andrea Torsello;Richard C. Wilson;Edwin R. Hancock
Affiliations:
Department of Computer Science, University of York, UK;Department of Environmental Science, Informatics and Statistics, Ca' Foscari Univerisity of Venice, Italy;Department of Environmental Science, Informatics and Statistics, Ca' Foscari Univerisity of Venice, Italy;Department of Computer Science, University of York, UK;Department of Computer Science, University of York, UK
Venue:
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Year:
2012

Citing 5
Cited 0

Structural Graph Matching Using the EM Algorithm and Singular Value Decomposition

IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Advances in Minimum Description Length: Theory and Applications (Neural Information Processing)

Advances in Minimum Description Length: Theory and Applications (Neural Information Processing)
A Supergraph-based Generative Model

ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Learning generative graph prototypes using simplified von neumann entropy

GbRPR'11 Proceedings of the 8th international conference on Graph-based representations in pattern recognition
Paper: Modeling by shortest data description

Automatica (Journal of IFAC)

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we propose a prototype size selection method for a set of sample graphs. Our first contribution is to show how approximate set coding can be extended from the vector to graph domain. With this framework to hand we show how prototype selection can be posed as optimizing the mutual information between two partitioned sets of sample graphs. We show how the resulting method can be used for prototype graph size selection. In our experiments, we apply our method to a real-world dataset and investigate its performance on prototype size selection tasks.