Graph embedding for pattern analysis

Graph Embedding for Pattern Recognition 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. Front Matter....Pages i-viii Multilevel Analysis of Attributed Graphs for Explicit Graph Embedding in Vector Spaces....Pages 1-26 Feature Grouping and Selection Over an Undirected Graph....Pages 27-43 Median Graph Computation by Means of Graph Embedding into Vector Spaces....Pages 45-71 Patch Alignment for Graph Embedding....Pages 73-118 Improving Classifications Through Graph Embeddings....Pages 119-138 Learning with l 1 -Graph for High Dimensional Data Analysis....Pages 139-156 Graph-Embedding Discriminant Analysis on Riemannian Manifolds for Visual Recognition....Pages 157-175 A Flexible and Effective Linearization Method for Subspace Learning....Pages 177-203 A Multi-graph Spectral Framework for Mining Multi-source Anomalies....Pages 205-227 Graph Embedding for Speaker Recognition....Pages 229-260 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