IDEAS home Printed from https://ideas.repec.org/a/spr/metrik/v87y2024i3d10.1007_s00184-023-00915-3.html
   My bibliography  Save this article

Robust distributed multicategory angle-based classification for massive data

Author

Listed:
  • Gaoming Sun

    (East China Normal University)

  • Xiaozhou Wang

    (East China Normal University
    East China Normal University)

  • Yibo Yan

    (East China Normal University)

  • Riquan Zhang

    (Shanghai University of International Business and Economics)

Abstract

Multicategory classification problems are frequently encountered in practice. Considering that the massive data sets are increasingly common and often stored locally, we first provide a distributed estimation in the multicategory angle-based classification framework and obtain its excess risk under general conditions. Further, under varied robustness settings, we develop two robust distributed algorithms to provide robust estimations of the multicategory classification. The first robust distributed algorithm takes advantage of median-of-means (MOM) and is designed by the MOM-based gradient estimation. The second robust distributed algorithm is implemented by constructing the weighted-based gradient estimation. The theoretical guarantees of our algorithms are established via the non-asymptotic error bounds of the iterative estimations. Some numerical simulations demonstrate that our methods can effectively reduce the impact of outliers.

Suggested Citation

  • Gaoming Sun & Xiaozhou Wang & Yibo Yan & Riquan Zhang, 2024. "Robust distributed multicategory angle-based classification for massive data," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 87(3), pages 299-323, April.
    • Handle: RePEc:spr:metrik:v:87:y:2024:i:3:d:10.1007_s00184-023-00915-3
    DOI: 10.1007/s00184-023-00915-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00184-023-00915-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s00184-023-00915-3?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:spr:metrik:v:87:y:2024:i:3:d:10.1007_s00184-023-00915-3. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.