• DocumentCode
    2783104
  • Title

    Unsupervised Learning Approaches for the Finite Mixture Models: EM versus MCMC

  • Author

    Liu, Weifeng

  • Author_Institution
    Sch. of Autom., Hangzhou Dianzi Univ., Hangzhou, China
  • fYear
    2010
  • fDate
    10-12 Oct. 2010
  • Firstpage
    498
  • Lastpage
    501
  • Abstract
    The key problem in unsupervised learning of finite mixture models is to estimate the parameters of the models. The expectation-maximization (EM) and Markov Chain Monte Carlo (MCMC) are usually used. In this paper, we review these two algorithms and give the complete algorithm processes. We also comment their advantage and disadvantage.
  • Keywords
    Markov processes; Monte Carlo methods; expectation-maximisation algorithm; unsupervised learning; EM; MCMC; Markov Chain Monte Carlo; expectation maximization; finite mixture models; unsupervised learning approaches; Algorithm design and analysis; Computational modeling; Equations; Estimation; Markov processes; Signal processing algorithms; Unsupervised learning;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), 2010 International Conference on
  • Conference_Location
    Huangshan
  • Print_ISBN
    978-1-4244-8434-8
  • Electronic_ISBN
    978-0-7695-4235-5
  • Type

    conf

  • DOI
    10.1109/CyberC.2010.96
  • Filename
    5616978
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2783104