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Erschienen in:
2011 | OriginalPaper | Buchkapitel
Vocabulary Selection for Graph of Words Embedding
verfasst von : Jaume Gibert, Ernest Valveny, Horst Bunke
Erschienen in: Pattern Recognition and Image Analysis
Verlag: Springer Berlin Heidelberg
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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.