IDEAS home Printed from https://ideas.repec.org/a/igg/jdst00/v9y2018i3p24-38.html
   My bibliography  Save this article

A Fast Distributed Non-Negative Matrix Factorization Algorithm Based on DSGD

Author

Listed:
  • Yan Gao

    (Central South University, Changsha, China)

  • Lingjun Zhou

    (Central South University, Changsha, China)

  • Baifan Chen

    (School of Information Science and Engineering, Central South University, Changsha, China)

  • Xiaobing Xing

    (Central South University, Changsha, China)

Abstract

In numerous solutions, the distributed stochastic gradient descent algorithm is one of the most popular algorithms for parallelization matrix decomposition. However, in parallel calculation, the computing speed of each computing node was greatly different because of the imbalance of the computing nodes. This article reduced the data skew for all computing nodes during distributed execution to solve the problem of locking waiting. The improved algorithm on DSGD was named D-DSGD, which reduced the time consumption of the algorithm and improved the utilization rate of the nodes. Meanwhile, the dynamic step size adjusting strategy was applied to improve the convergence rate of the algorithm. To ensure the non-negative matrix decomposition, non-negative control was added into D-DSGD and the improved algorithm was named D-NMF. Compared with the existing methods, the proposed algorithm in this article has a marked impact on reducing the latency and speed of convergence.

Suggested Citation

  • Yan Gao & Lingjun Zhou & Baifan Chen & Xiaobing Xing, 2018. "A Fast Distributed Non-Negative Matrix Factorization Algorithm Based on DSGD," International Journal of Distributed Systems and Technologies (IJDST), IGI Global, vol. 9(3), pages 24-38, July.
    • Handle: RePEc:igg:jdst00:v:9:y:2018:i:3:p:24-38
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJDST.2018070102
    Download Restriction: no
    ---><---

    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:igg:jdst00:v:9:y:2018:i:3:p:24-38. 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.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.