Graph Embedding for Pattern Analysis
Douban
Yun Fu / Yunqian Ma
overview
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.
contents
Multilevel Analysis of Attributed Graphs for Explicit Graph Embedding in Vector Spaces
Luqman, Muhammad Muzzamil (et al.)
Pages 1-26
Feature Grouping and Selection Over an Undirected Graph
Yang, Sen (et al.)
Pages 27-43
Median Graph Computation by Means of Graph Embedding into Vector Spaces
Ferrer, Miquel (et al.)
Pages 45-71
Patch Alignment for Graph Embedding
Luo, Yong (et al.)
Pages 73-118
Improving Classifications Through Graph Embeddings
Chatterjee, Anirban (et al.)
Pages 119-138
Learning with ℓ 1-Graph for High Dimensional Data Analysis
Yang, Jianchao (et al.)
Pages 139-156
Graph-Embedding Discriminant Analysis on Riemannian Manifolds for Visual Recognition
Shirazi, Sareh (et al.)
Pages 157-175
A Flexible and Effective Linearization Method for Subspace Learning
Nie, Feiping (et al.)
Pages 177-203
A Multi-graph Spectral Framework for Mining Multi-source Anomalies
Gao, Jing (et al.)
Pages 205-227
Graph Embedding for Speaker Recognition
Karam, Z. N. (et al.)
Pages 229-260