Graph classification and clustering based on vector space embedding
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 …
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- Full title :Graph classification and clustering based on vector space embedding / Kaspar Riesen & Horst Bunke
Summary
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.
Read More ↓Edition Statements
- Publication Date:2010
Publication Statements
- Publisher(s):World Scientific Pub. Co
- Place of Publication:Singapore ;Hackensack, N.J
- Language:eng
- Publication Date:2010
Notes
Includes bibliographical references and index
The Physical Object
- Description:Online-Ressource (xiv, 331 p) : ill.
Edition Identifiers
- ISBN:9789814304719, 9789814304726, 9814304727, 9814304719
- oclc:ocn838896811
- LC Classification:QC793.3.S8
- Dewey Decimal:006.42
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