Graph Embedding for Pattern Analysis

Graph Embedding for Pattern Analysis covers theory methods, computation, and applications widely used in statistics, machine learning, image processing, and computer vision. This book presents the latest advances in graph embedding theories, such as nonlinear manifold graph, linearization method, gr...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor Corporativo: SpringerLink (Online service)
Otros Autores: Fu, Yun (Editor ), Ma, Yunqian (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: New York, NY : Springer New York : Imprint: Springer, 2013.
Edición:1st ed. 2013.
Temas:
Telecommunication.
Pattern recognition systems.
Artificial intelligence.
Signal processing.
Communications Engineering, Networks.
Automated Pattern Recognition.
Artificial Intelligence.
Signal, Speech and Image Processing .
Acceso en línea:Texto Completo

MARC

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245 1 0 |a Graph Embedding for Pattern Analysis  |h [electronic resource] /  |c edited by Yun Fu, Yunqian Ma. 
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300 |a VIII, 260 p.  |b online resource. 
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505 0 |a Multilevel Analysis of Attributed Graphs for Explicit Graph Embedding in Vector Spaces -- Feature Grouping and Selection over an Undirected Graph -- Median Graph Computation by Means of Graph Embedding into Vector Spaces -- Patch Alignment for Graph Embedding -- Feature Subspace Transformations for Enhancing K-Means Clustering -- Learning with ℓ1-Graph for High Dimensional Data Analysis -- Graph-Embedding Discriminant Analysis on Riemannian Manifolds for Visual Recognition -- A Flexible and Effective Linearization Method for Subspace Learning -- A Multi-Graph Spectral Approach for Mining Multi-Source Anomalies -- Graph Embedding for Speaker Recognition. 
520 |a Graph Embedding for Pattern Analysis covers theory methods, computation, and applications widely used in statistics, machine learning, image processing, and computer vision. This book presents the latest advances in graph embedding theories, such as nonlinear manifold graph, linearization method, graph based subspace analysis, L1 graph, hypergraph, undirected graph, and graph in vector spaces. Real-world applications of these theories are spanned broadly in dimensionality reduction, subspace learning, manifold learning, clustering, classification, and feature selection. A selective group of experts contribute to different chapters of this book which provides a comprehensive perspective of this field. 
650 0 |a Telecommunication. 
650 0 |a Pattern recognition systems. 
650 0 |a Artificial intelligence. 
650 0 |a Signal processing. 
650 1 4 |a Communications Engineering, Networks. 
650 2 4 |a Automated Pattern Recognition. 
650 2 4 |a Artificial Intelligence. 
650 2 4 |a Signal, Speech and Image Processing . 
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700 1 |a Ma, Yunqian.  |e editor.  |4 edt  |4 http://id.loc.gov/vocabulary/relators/edt 
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950 |a Engineering (SpringerNature-11647) 
950 |a Engineering (R0) (SpringerNature-43712) 

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