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Graph Embedding for Pattern Analysis

  • Book
  • © 2013

Overview

Editors:
  1. Yun Fu

    1. , Dept. of ECE, College of Engineering, Northeastern University, Boston, USA

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    You can also search for this editor in PubMed   Google Scholar

  2. Yunqian Ma

    1. Honeywell, Golden Valley, USA

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    You can also search for this editor in PubMed   Google Scholar

  • 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

  • Includes supplementary material: sn.pub/extras

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Table of contents (10 chapters)

  1. Front Matter

    Pages i-viii
    Download chapter PDF

  2. Multilevel Analysis of Attributed Graphs for Explicit Graph Embedding in Vector Spaces

    • Muhammad Muzzamil Luqman, Jean-Yves Ramel, Josep Lladós
    Pages 1-26

  3. Feature Grouping and Selection Over an Undirected Graph

    • Sen Yang, Lei Yuan, Ying-Cheng Lai, Xiaotong Shen, Peter Wonka, Jieping Ye
    Pages 27-43

  4. Median Graph Computation by Means of Graph Embedding into Vector Spaces

    • Miquel Ferrer, Itziar Bardají, Ernest Valveny, Dimosthenis Karatzas, Horst Bunke
    Pages 45-71

  5. Patch Alignment for Graph Embedding

    • Yong Luo, Dacheng Tao, Chao Xu
    Pages 73-118

  6. Improving Classifications Through Graph Embeddings

    • Anirban Chatterjee, Sanjukta Bhowmick, Padma Raghavan
    Pages 119-138

  7. Learning with ℓ 1-Graph for High Dimensional Data Analysis

    • Jianchao Yang, Bin Cheng, Shuicheng Yan, Yun Fu, Thomas Huang
    Pages 139-156

  8. Graph-Embedding Discriminant Analysis on Riemannian Manifolds for Visual Recognition

    • Sareh Shirazi, Azadeh Alavi, Mehrtash T. Harandi, Brian C. Lovell
    Pages 157-175

  9. A Flexible and Effective Linearization Method for Subspace Learning

    • Feiping Nie, Dong Xu, Ivor W. Tsang, Changshui Zhang
    Pages 177-203

  10. A Multi-graph Spectral Framework for Mining Multi-source Anomalies

    • Jing Gao, Nan Du, Wei Fan, Deepak Turaga, Srinivasan Parthasarathy, Jiawei Han
    Pages 205-227

  11. Graph Embedding for Speaker Recognition

    • Z. N. Karam, W. M. Campbell
    Pages 229-260
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Keywords

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.

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)

Editors and Affiliations

  • , Dept. of ECE, College of Engineering, Northeastern University, Boston, USA

    Yun Fu

  • Honeywell, Golden Valley, USA

    Yunqian Ma

About the editors

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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