Dimensionality reduction for graph of words embedding

Jaume Gibert, Ernest Valveny, Horst Bunke

Research output: Contribution to journalArticleResearchpeer-review

15 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)22-31
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6658 LNCS
Publication statusPublished - 26 May 2011

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