Graph Embedding for Pattern Analysis

  • Yun Fu
  • Yunqian Ma

Table of contents

  1. Front Matter
    Pages i-viii
  2. Muhammad Muzzamil Luqman, Jean-Yves Ramel, Josep Lladós
    Pages 1-26
  3. Sen Yang, Lei Yuan, Ying-Cheng Lai, Xiaotong Shen, Peter Wonka, Jieping Ye
    Pages 27-43
  4. Miquel Ferrer, Itziar Bardají, Ernest Valveny, Dimosthenis Karatzas, Horst Bunke
    Pages 45-71
  5. Yong Luo, Dacheng Tao, Chao Xu
    Pages 73-118
  6. Anirban Chatterjee, Sanjukta Bhowmick, Padma Raghavan
    Pages 119-138
  7. Jianchao Yang, Bin Cheng, Shuicheng Yan, Yun Fu, Thomas Huang
    Pages 139-156
  8. Sareh Shirazi, Azadeh Alavi, Mehrtash T. Harandi, Brian C. Lovell
    Pages 157-175
  9. Feiping Nie, Dong Xu, Ivor W. Tsang, Changshui Zhang
    Pages 177-203
  10. Jing Gao, Nan Du, Wei Fan, Deepak Turaga, Srinivasan Parthasarathy, Jiawei Han
    Pages 205-227
  11. Z. N. Karam, W. M. Campbell
    Pages 229-260

About this book

Introduction

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.

Keywords

Computer Vision Dimensionality Reduction Discriminant Analysis Graph Embedding Hypergraph Machine Learning Manifold Learning Pattern Recognition Subspace Learning

Editors and affiliations

  • Yun Fu
    • 1
  • Yunqian Ma
    • 2
  1. 1., Dept. of ECE, College of EngineeringNortheastern UniversityBostonUSA
  2. 2.HoneywellGolden ValleyUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4614-4457-2
  • Copyright Information Springer Science+Business Media New York 2013
  • Publisher Name Springer, New York, NY
  • eBook Packages Engineering
  • Print ISBN 978-1-4614-4456-5
  • Online ISBN 978-1-4614-4457-2
  • About this book
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