Graph Embedding for Pattern Analysis 2013th Edition
Author(s): Yun Fu (Editor), Yunqian Ma
Publisher: Springer
Publication Date: 17 Nov. 2012
Edition: 2013th
Language: English
Print length: 268 pages
ISBN-10: 146144456X
ISBN-13: 9781461444565
Book Description
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.
Editorial Reviews
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 2013th Edition 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.
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