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Hyper-parameter Optimization of Sticky HDP-HMM Through an Enhanced Particle Swarm Optimization

  • Jiaxi LiEmail author
  • Junfu Yin
  • Yuk Ying Chung
  • Feng Sha
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9949)

Abstract

Faced with the problem of uncertainties in object trajectory and pattern recognition in terms of the non-parametric Bayesian approach, we have derived that 2 major methods of optimizing hierarchical Dirichlet process hidden Markov model (HDP-HMM) for the task. HDP-HMM suffers from poor performance not only on moderate dimensional data, but also sensitivity to its parameter settings. For the purpose of optimizing HDP-HMM on dimensional data, test for optimized results will be carried on the Tum Kitchen dataset [7], which was provided for the purpose of research the motion and activity recognitions. The optimization techniques capture the best hyper-parameters which then produce optimal solution to the task given in a certain search space.

Keywords

Non-parametric Bayes HDP-HMM Pattern recognition Model selection Optimization Hyper-parameters 

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Jiaxi Li
    • 1
    Email author
  • Junfu Yin
    • 1
  • Yuk Ying Chung
    • 1
  • Feng Sha
    • 1
  1. 1.School of Information TechnologiesUniversity of SydneySydneyAustralia

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