Springer Graph Embedding for Pattern Analysis

This book presents 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, and describes their real-world applications. Review From the reviews: “The papers in this collection apply the methods elaborated in classical and algebraic graph theory to analyze patterns in various contexts. … the book will be easy for a researcher well versed in the theoretical fundamentals of the presented methods. … the editors have been able to structure the contents in an effective and interesting way. Therefore, I can recommend this volume as a useful reference for specialists in the field.” (Piotr Cholda, Computing Reviews, November, 2013) From the Back Cover 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. About the Author Dr. Yun Fu is a professor at the State University of New York at Buffalo Dr. Yunqian Ma is a senior principal research scientist of Honeywell Labs at the Honeywell International Inc.