PT Unknown
AU Jaume Gibert
   Ernest Valveny
   Horst Bunke
TI Dimensionality Reduction for Graph of Words Embedding
BT 8th IAPR-TC-15 International Workshop. Graph-Based Representations in Pattern Recognition
PY 2011
BP 22
EP 31
VL 6658
DI http://dx.doi.org/10.1007/978-3-642-20844-7_3
AB 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.
ER