@inproceedings{d0012e276d204e1e88ca65e76805adb1,
title = "Information theoretic prototype selection for unattributed graphs",
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.",
keywords = "Importance Sampling, Mutual information, Partition function, Prototype Selection",
author = "Lin Han and Luca Rossi and Andrea Torsello and Wilson, {Richard C.} and Hancock, {Edwin R.}",
year = "2012",
month = nov,
doi = "10.1007/978-3-642-34166-3_4",
language = "English",
isbn = "9783642341656",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "33--41",
booktitle = "Structural, Syntactic, and Statistical Pattern Recognition - Joint IAPR International Workshop, SSPR and SPR 2012, Proceedings",
note = "Joint IAPR International Workshops on Structural and Syntactic PatternRecognition, SSPR 2012 and Statistical Techniques in Pattern Recognition,SPR 2012 ; Conference date: 07-11-2012 Through 09-11-2012",
}