IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/176058.html
   My bibliography  Save this article

An Efficient Feature Extraction Method, Global Between Maximum and Local Within Minimum, and Its Applications

Author

Listed:
  • Lei Wang
  • Jiangshe Zhang
  • Fei Zang

Abstract

Feature extraction plays an important role in preprocessing procedure in dealing with small sample size problems. Considering the fact that LDA, LPP, and many other existing methods are confined to one case of the data set. To solve this problem, we propose an efficient method in this paper, named global between maximum and local within minimum. It not only considers the global structure of the data set, but also makes the best of the local geometry of the data set through dividing the data set into four domains. This method preserves relations of the nearest neighborhood, as well as demonstrates an excellent performance in classification. Superiority of the proposed method in this paper is manifested in many experiments on data visualization, face representative, and face recognition.

Suggested Citation

  • Lei Wang & Jiangshe Zhang & Fei Zang, 2011. "An Efficient Feature Extraction Method, Global Between Maximum and Local Within Minimum, and Its Applications," Mathematical Problems in Engineering, Hindawi, vol. 2011, pages 1-15, July.
  • Handle: RePEc:hin:jnlmpe:176058
    DOI: 10.1155/2011/176058
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2011/176058.pdf
    Download Restriction: no

    File URL: http://downloads.hindawi.com/journals/MPE/2011/176058.xml
    Download Restriction: no

    File URL: https://libkey.io/10.1155/2011/176058?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hin:jnlmpe:176058. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.