TY  - CONF
AU  - Jaume Gibert
AU  - Ernest Valveny
AU  - Horst Bunke
A2  - GbRPR
ED  - Xiaoyi Jiang
ED  - Miquel Ferrer
ED  - Andrea Torsello
PY  - 2011//
TI  - Dimensionality Reduction for Graph of Words Embedding
T2  - LNCS
BT  - 8th IAPR-TC-15 International Workshop. Graph-Based Representations in Pattern Recognition
SP  - 22
EP  - 31
VL  - 6658
N2  - 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.
SN  - 978-3-642-20843-0
UR  - http://dx.doi.org/10.1007/978-3-642-20844-7_3
N1  - DAG
ID  - Jaume Gibert2011
ER  -