Accelerating generalized iterative scaling based on staggered aitken method for on-line conditional random fields

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In this paper, a convergent method based on Generalized Iterative Scaling (GIS) with staggered Aitken acceleration is proposed to estimate the parameters for an on-line Conditional Random Field (CRF). The staggered Aitken acceleration method, which alternates between the acceleration and non-acceleration steps, ensures computational simplicity when analyzing incomplete data. The proposed method has the following advantages: (1) It can approximate parameters close to the empirical optimum in a single pass through the training examples; (2) It can reduce the computing time by approximating the Jacobian matrix of the mapping function and estimating the relation between the Jacobian and Hessian in order to replace the inverse of the objective function's Hessian matrix. We show the convergence of the penalized GIS based on the staggered Aitken acceleration method, compare its speed of convergence with those of other stochastic optimization methods, and illustrate experimental results with two public datasets.

Original languageEnglish
Article number1250059
JournalInternational Journal of Wavelets, Multiresolution and Information Processing
Issue number6
Publication statusPublished - 2012 Nov

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) Grant funded by the Ministry of Education, Science, and Technology (MEST), under Grant 2012-005741. This work was also supported by the NRF Grant funded by the Korean government (MEST) (No. 2010-0015362).


  • Aitken acceleration
  • On-line conditional random field
  • sequence labeling

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems
  • Applied Mathematics


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