• DocumentCode
    659502
  • Title

    Practical distributed classification using the Alternating Direction Method of Multipliers algorithm

  • Author

    Lubell-Doughtie, Peter ; Sondag, Jon

  • Author_Institution
    Intent Media, New York, NY, USA
  • fYear
    2013
  • fDate
    6-9 Oct. 2013
  • Firstpage
    773
  • Lastpage
    776
  • Abstract
    We describe a specific implementation of the Alternating Direction Method of Multipliers (ADMM) algorithm for distributed optimization. This implementation runs logistic regression with L2 regularization over large datasets and does not require a user-tuned learning rate metaparameter or any tools beyond MapReduce. Throughout we emphasize the practical lessons learned while implementing an iterative MapReduce algorithm and the advantages of remaining within the Hadoop ecosystem.
  • Keywords
    distributed algorithms; iterative methods; optimisation; pattern classification; regression analysis; Hadoop ecosystem; L2 regularization; alternating direction method of multipliers algorithm; distributed classification; distributed optimization; iterative MapReduce algorithm; logistic regression; Clustering algorithms; Data models; Logistics; Optimization; Prediction algorithms; Predictive models; Vectors; distributed algorithms; distributed computing; optimization; predictive models;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Big Data, 2013 IEEE International Conference on
  • Conference_Location
    Silicon Valley, CA
  • Type

    conf

  • DOI
    10.1109/BigData.2013.6691651
  • Filename
    6691651
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=659502