Subspace Methods for Pattern Recognition in Intelligent Environment

  • Yen-Wei Chen
  • Lakhmi C. Jain

Part of the Studies in Computational Intelligence book series (SCI, volume 552)

Table of contents

  1. Front Matter
    Pages 1-14
  2. Elco Oost, Sho Tomoshige, Akinobu Shimizu
    Pages 33-56
  3. Hiroyuki Ishida, Ichiro Ide, Hiroshi Murase
    Pages 83-104
  4. Xian-Hua Han, Yen-Wei Chen
    Pages 123-150
  5. Xu Qiao, Takanori Igarashi, Yen-Wei Chen
    Pages 171-195
  6. Back Matter
    Pages 197-199

About this book


This research book provides a comprehensive overview of the state-of-the-art subspace learning methods for pattern recognition in intelligent environment. With the fast development of internet and computer technologies, the amount of available data is rapidly increasing in our daily life. How to extract core information or useful features is an important issue. Subspace methods are widely used for dimension reduction and feature extraction in pattern recognition. They transform a high-dimensional data to a lower-dimensional space (subspace), where most information is retained. The book covers a broad spectrum of subspace methods including linear, nonlinear and multilinear subspace learning methods and applications. The applications include face alignment, face recognition, medical image analysis, remote sensing image classification, traffic sign recognition, image clustering, super resolution, edge detection, multi-view facial image synthesis.


Computational Intelligence Knowledge-Based Techniques Pattern Recognition Subspace Methods

Editors and affiliations

  • Yen-Wei Chen
    • 1
  • Lakhmi C. Jain
    • 2
  1. 1.Ritsumeikan University College of Science & EngineeringKusuatsu, ShigaJapan
  2. 2.Faculty of Education, Science, Technology & MathematicsUniversity of CanberraCanberraAustralia

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag Berlin Heidelberg 2014
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering Engineering (R0)
  • Print ISBN 978-3-642-54850-5
  • Online ISBN 978-3-642-54851-2
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • Buy this book on publisher's site
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