Vocabulary selection for graph of words embedding

Authors:
Jaume Gibert;Ernest Valveny;Horst Bunke
Affiliations:
Computer Vision Center, Universitat Autònoma de Barcelona, Bellaterra, Spain;Computer Vision Center, Universitat Autònoma de Barcelona, Bellaterra, Spain;Institute for Computer Science and Applied Mathematics, University of Bern, Bern, Switzerland
Venue:
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
Year:
2011

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Abstract

The Graph of Words Embedding consists in mapping every graph in a given dataset to a feature vector by counting unary and binary relations between node attributes of the graph. It has been shown to perform well for graphs with discrete label alphabets. In this paper we extend the methodology to graphs with n-dimensional continuous attributes by selecting node representatives. We propose three different discretization procedures for the attribute space and experimentally evaluate the dependence on both the selector and the number of node representatives. In the context of graph classification, the experimental results reveal that on two out of three public databases the proposed extension achieves superior performance over a standard reference system.