• DocumentCode
    158535
  • Title

    Variable selection by RIVAL

  • Author

    Er-Wei Bai ; Kang Li ; Kump, Paul

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Univ. of Iowa, Iowa City, IA, USA
  • fYear
    2014
  • fDate
    16-19 June 2014
  • Firstpage
    913
  • Lastpage
    917
  • Abstract
    The paper considers variable selection problem and proposes an algorithm called the RIVAL (Removing Irrelevant Variables Amidst Lasso Iterations). For a given and fixed length of data points, the algorithm recursively updates the weights so that the ability of the algorithm in detecting zero coefficients is substantially improved. Theoretical convergence is established supported by numerical simulation results.
  • Keywords
    iterative methods; recursive estimation; RIVAL; data point fixed length; removing irrelevant variables amidst Lasso iterations; variable selection; zero coefficients detection; Adaptation models; Convergence; Educational institutions; Indexes; Input variables; Numerical simulation; Vectors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Control and Automation (MED), 2014 22nd Mediterranean Conference of
  • Conference_Location
    Palermo
  • Print_ISBN
    978-1-4799-5900-6
  • Type

    conf

  • DOI
    10.1109/MED.2014.6961490
  • Filename
    6961490
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=158535