Low-Rank and Sparse Modeling for Visual Analysis

  • Yun Fu

Table of contents

  1. Front Matter
    Pages i-vii
  2. Ivan Markovsky, Konstantin Usevich
    Pages 1-22
  3. Guangcan Liu, Shuicheng Yan
    Pages 23-38
  4. Guangcan Liu, Shuicheng Yan
    Pages 39-60
  5. Sheng Li, Liangyue Li, Yun Fu
    Pages 61-85
  6. Ming Shao, Dmitry Kit, Yun Fu
    Pages 87-115
  7. Ming Shao, Mingbo Ma, Yun Fu
    Pages 117-132
  8. Guoqiang Zhong, Mohamed Cheriet
    Pages 133-150
  9. Jianhui Chen, Jiayu Zhou, Jieping Ye
    Pages 151-180
  10. Sheng Li, Ming Shao, Yun Fu
    Pages 181-202
  11. Yang Cong, Ji Liu, Junsong Yuan, Jiebo Luo
    Pages 203-233
  12. Back Matter
    Pages 235-236

About this book


This book provides a view of low-rank and sparse computing, especially approximation, recovery, representation, scaling, coding, embedding, and learning among unconstrained visual data. Included in the book are chapters covering multiple emerging topics in this new field. The text links multiple popular research fields in Human-Centered Computing, Social Media, Image Classification, Pattern Recognition, Computer Vision, Big Data, and Human-Computer Interaction. This book contains an overview of the low-rank and sparse modeling techniques for visual analysis by examining both theoretical analysis and real-world applications.

·         Covers the most state-of-the-art topics of sparse and low-rank modeling

·         Examines the theory of sparse and low-rank analysis to the real-world practice of sparse and low-rank analysis

·         Contributions from top experts voicing their unique perspectives included throughout


Compressive Sensing Computer Vision Dimensionality Reduction Low-Rank Approximation Low-Rank Recover Low-Rank Representation Machine Learning Pattern Recognition Sparse Representation Subspace Learning

Editors and affiliations

  • Yun Fu
    • 1
  1. 1.Northeastern UniversityBostonUSA

Bibliographic information

  • DOI
  • Copyright Information Springer International Publishing Switzerland 2014
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-11999-1
  • Online ISBN 978-3-319-12000-3
  • Buy this book on publisher's site