
Graph Classification And Clustering Based On Vector Space Embedding
World Scientific Publishing Co Pte Ltd
Published on 3. May 2010
Book
Hardback
348 pages
978-981-4304-71-9 (ISBN)
Description
This 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.
More details
Series
Language
English
Place of publication
Singapore
Singapore
Target group
College/higher education
Professional and scholarly
Dimensions
Height: 235 mm
Width: 157 mm
Thickness: 23 mm
Weight
655 gr
ISBN-13
978-981-4304-71-9 (9789814304719)
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Schweitzer Classification
Persons
Author
Univ Of Bern, Switzerland & Univ Of Applied Sciences & Arts Northwestern, Switzerland
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Content
Introduction 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.