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Graph classification and clustering based on vector space embedding [electronic resource] / Kaspar Riesen & Horst Bunke.

By: Riesen, Kaspar.
Contributor(s): Bunke, Horst.
Material type: TextTextSeries: Series in machine perception and artificial intelligence: v. 77.Publisher: Singapore ; London : World Scientific, c2010Description: 1 online resource (xiv, 331 p.) : ill.ISBN: 9789814304726 (electronic bk.); 9814304727 (electronic bk.).Subject(s): Optical pattern recognition | Artificial intelligence -- Graphic methods | Vector spaces | Civil engineering | Computer science | Engineering | COMPUTERS -- Optical Data Processing | COMPUTERS -- Computer Vision & Pattern RecognitionGenre/Form: Electronic books. | Electronic books.Additional physical formats: Print version:: Graph classification and clustering based on vector space embedding.DDC classification: 006.42 Online resources: EBSCOhost
Contents:
1. Introduction and basic concepts. 1.1. Pattern recognition. 1.2. Learning methodology. 1.3. Statistical and structural pattern recognition. 1.4. Dissimilarity representation for pattern recognition. 1.5. Summary and outline -- 2. Graph matching. 2.1. Graph and subgraph. 2.2. Exact graph matching. 2.3. Error-tolerant graph matching. 2.4. Summary and broader perspective -- 3. Graph edit distance. 3.1. Basic definition and properties. 3.2. Exact computation of GED. 3.3. Efficient approximation algorithms. 3.4. Exact vs. approximate graph edit distance -- an experimental evaluation. 3.5. Summary -- 4. Graph data. 4.1. Graph data sets. 4.2. Evaluation of graph edit distance. 4.3. Data visualization. 4.4. Summary -- 5. Kernel methods. 5.1. Overview and primer on kernel theory. 5.2. Kernel functions. 5.3. Feature map vs. kernel trick. 5.4. Kernel machines. 5.5. Graph kernels. 5.6. Experimental evaluation. 5.7. Summary -- 6. Graph embedding using dissimilarities. 6.1. Related work. 6.2. Graph embedding using dissimilarities. 6.3. Prototype selection strategies. 6.4. Prototype reduction schemes. 6.5. Feature selection algorithms. 6.6. Defining the reference sets for Lipschitz embeddings. 6.7. Ensemble methods. 6.8. Summary -- 7. Classification experiments with vector space embedded graphs. 7.1. Nearest-neighbor classifiers applied to vector space embedded graphs. 7.2. Support vector machines applied to vector space embedded graphs. 7.3. Summary and discussion -- 8. Clustering experiments with vector space embedded graphs. 8.1. Experimental setup and validation of the meta parameters. 8.2. Results and discussion. 8.3. Summary and discussion -- 9. Conclusions.
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.
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Includes bibliographical references (p. 309-328) and index.

Description based on print version record.

1. Introduction and basic concepts. 1.1. Pattern recognition. 1.2. Learning methodology. 1.3. Statistical and structural pattern recognition. 1.4. Dissimilarity representation for pattern recognition. 1.5. Summary and outline -- 2. Graph matching. 2.1. Graph and subgraph. 2.2. Exact graph matching. 2.3. Error-tolerant graph matching. 2.4. Summary and broader perspective -- 3. Graph edit distance. 3.1. Basic definition and properties. 3.2. Exact computation of GED. 3.3. Efficient approximation algorithms. 3.4. Exact vs. approximate graph edit distance -- an experimental evaluation. 3.5. Summary -- 4. Graph data. 4.1. Graph data sets. 4.2. Evaluation of graph edit distance. 4.3. Data visualization. 4.4. Summary -- 5. Kernel methods. 5.1. Overview and primer on kernel theory. 5.2. Kernel functions. 5.3. Feature map vs. kernel trick. 5.4. Kernel machines. 5.5. Graph kernels. 5.6. Experimental evaluation. 5.7. Summary -- 6. Graph embedding using dissimilarities. 6.1. Related work. 6.2. Graph embedding using dissimilarities. 6.3. Prototype selection strategies. 6.4. Prototype reduction schemes. 6.5. Feature selection algorithms. 6.6. Defining the reference sets for Lipschitz embeddings. 6.7. Ensemble methods. 6.8. Summary -- 7. Classification experiments with vector space embedded graphs. 7.1. Nearest-neighbor classifiers applied to vector space embedded graphs. 7.2. Support vector machines applied to vector space embedded graphs. 7.3. Summary and discussion -- 8. Clustering experiments with vector space embedded graphs. 8.1. Experimental setup and validation of the meta parameters. 8.2. Results and discussion. 8.3. Summary and discussion -- 9. Conclusions.

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.

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Graph classification and clustering based on vector space embedding by Riesen, Kaspar. ©2010
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