TY - JOUR
T1 - Dimensionality reduction for graph of words embedding
AU - Gibert, Jaume
AU - Valveny, Ernest
AU - Bunke, Horst
PY - 2011/5/26
Y1 - 2011/5/26
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. © 2011 Springer-Verlag Berlin Heidelberg.
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. © 2011 Springer-Verlag Berlin Heidelberg.
U2 - 10.1007/978-3-642-20844-7_3
DO - 10.1007/978-3-642-20844-7_3
M3 - Article
SN - 0302-9743
VL - 6658 LNCS
SP - 22
EP - 31
JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ER -