Accelerating EM by targeted aggressive double extrapolation

Han Shen Huang*, Bo Hou Yang, Ren Yuan Lyu, Chun Nan Hsu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

The Expectation-Maximization (EM) algorithm is one of the most popular algorithms for parameter estimation from incomplete data, but its convergence can be slowfor some largescale or complex problems. Extrapolation methods can effectively accelerate EM, but to ensure stability, the learning rate of extrapolation must be compromised. This paper describes the TJ2aEM method, a targeted extrapolation method that can extrapolate much more aggressively than competing methods without causing instability problems. We analyze its convergence properties and report experimental results.

Original languageEnglish
Title of host publication2009 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings, ICASSP 2009
Pages1609-1612
Number of pages4
DOIs
StatePublished - 2009
Event2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009 - Taipei, Taiwan
Duration: 19 04 200924 04 2009

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009
Country/TerritoryTaiwan
CityTaipei
Period19/04/0924/04/09

Keywords

  • Acceleration
  • Convergence of numerical methods
  • Eigenvalues and eigenfunctions
  • Extrapolation
  • Parameter estimation

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