A General Strategy for Hidden Markov Chain Parameterisation in Composite Feature-Spaces

  • David Windridge
  • Richard Bowden
  • Josef Kittler
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3138)


A general technique for the construction of hidden Markov models (HMMs) from multiple-variable time-series observations in noisy experimental environments is set out. The proposed methodology provides an ICA-based feature-selection technique for determining the number, and the transition sequence, of underlying hidden states, along with the statistics of the observed-state emission characteristics. In retaining correlation information between features, the method is potentially far more general than Gaussian mixture model HMM parameterisation methods such as Baum-Welch re-estimation, to which we demonstrate our method reduces when an arbitrary separation of features, or an experimentally-limited feature-space is imposed.


Hide Markov Model Independent Component Analysis Gaussian Mixture Model Independent Component Analysis Hide State 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • David Windridge
    • 1
  • Richard Bowden
    • 1
  • Josef Kittler
    • 1
  1. 1.Centre for Vision, Speech and Signal Processing, Dept. of Electronic & Electrical EngineeringUniversity of SurreyGuildfordUK

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