%0 Conference Proceedings
%T Dimensionality Reduction for Graph of Words Embedding
%A Jaume Gibert
%A Ernest Valveny
%A Horst Bunke
%E Xiaoyi Jiang
%E Miquel Ferrer
%E Andrea Torsello
%B 8th IAPR-TC-15 International Workshop. Graph-Based Representations in Pattern Recognition
%D 2011
%V 6658
%@ 978-3-642-20843-0
%F Jaume Gibert2011
%O DAG
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=1743), last updated on Thu, 17 May 2012 10:13:55 +0200
%X The Graph of Words Embedding consists in mapping every graph of a given dataset to a feature vector by counting unary and binary relations between node attributes of the graph. While it shows good properties in classification problems, it suffers from high dimensionality and sparsity. These two issues are addressed in this article. Two well-known techniques for dimensionality reduction, kernel principal component analysis (kPCA) and independent component analysis (ICA), are applied to the embedded graphs. We discuss their performance compared to the classification of the original vectors on three different public databases of graphs.
%U http://dx.doi.org/10.1007/978-3-642-20844-7_3
%P 22-31