Description
Book SynopsisThis book is concerned with a fundamentally novel approach to graph-based pattern recognition based on vector space embedding of graphs. It aims at condensing the high representational power of graphs into a computationally efficient and mathematically convenient feature vector.This volume utilizes the dissimilarity space representation originally proposed by Duin and Pekalska to embed graphs in real vector spaces. Such an embedding gives one access to all algorithms developed in the past for feature vectors, which has been the predominant representation formalism in pattern recognition and related areas for a long time.
Table of ContentsIntroduction and Basic Concepts; Graph Matching; Graph Edit Distance; Graph Data; Kernel Methods; Graph Embedding Using Dissimilarities; Classification Experiments of Vector Space Embedded Graphs; Clustering Experiments of Vector Space Embedded Graphs.
