Graph Embedding for Pattern Analysis [electronic resource] / edited by Yun Fu, Yunqian Ma.

By: Fu, Yun [editor.]Contributor(s): Ma, Yunqian [editor.] | SpringerLink (Online service)Material type: TextTextLanguage: English Publisher: New York, NY : Springer New York : Imprint: Springer, 2013Description: VIII, 260 p. 91 illus., 63 illus. in color. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9781461444572Subject(s): Engineering | Artificial intelligence | Optical pattern recognition | Telecommunication | Engineering | Communications Engineering, Networks | Pattern Recognition | Artificial Intelligence (incl. Robotics) | Signal, Image and Speech ProcessingAdditional physical formats: Printed edition:: No titleDDC classification: 621.382 LOC classification: TK1-9971Online resources: Click here to access online
Contents:
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.
In: Springer eBooksSummary: 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.
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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.

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.

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