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

Jiaxi Li, Junfu Yin, Yuk Ying Chung, Feng Sha

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.