Graph Embedding for Pattern Analysis

Editors: Fu, Yun, Ma, Yunqian (Eds.)

  • Covers theoretical analysis and real-world applications for graph embedding
  • Examines subspace analysis with L1 graph
  • Describes graph-based inference on Riemannian manifolds for visual analysis
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About this book

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.

About the authors

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.

Reviews

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)


Table of contents (10 chapters)

  • 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

Buy this book

eBook $119.00
price for USA (gross)
  • ISBN 978-1-4614-4457-2
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $159.99
price for USA
  • ISBN 978-1-4614-4456-5
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $139.99
price for USA
  • ISBN 978-1-4899-9062-4
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Rent the eBook  
  • Rental duration: 1 or 6 month
  • low-cost access
  • online reader with highlighting and note-making option
  • can be used across all devices
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Bibliographic Information

Bibliographic Information
Book Title
Graph Embedding for Pattern Analysis
Editors
  • Yun Fu
  • Yunqian Ma
Copyright
2013
Publisher
Springer-Verlag New York
Copyright Holder
Springer Science+Business Media New York
eBook ISBN
978-1-4614-4457-2
DOI
10.1007/978-1-4614-4457-2
Hardcover ISBN
978-1-4614-4456-5
Softcover ISBN
978-1-4899-9062-4
Edition Number
1
Number of Pages
VIII, 260
Topics