Paper Info
Title
Hyper-parameter Optimization of Sticky HDP-HMM Through an Enhanced Particle Swarm Optimization.
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.
Year
DOI
Venue
2016
10.1007/978-3-319-46675-0_11
Lecture Notes in Computer Science
Keywords
Field
DocType
Non-parametric Bayes,HDP-HMM,Pattern recognition,Model selection,Optimization,Hyper-parameters
Particle swarm optimization,Hierarchical Dirichlet process,Pattern recognition,Hyperparameter,Computer science,Algorithm,Model selection,Multi-swarm optimization,Artificial intelligence,Hidden Markov model,Trajectory,Bayesian probability
Conference
Volume
ISSN
Citations 
9949
0302-9743
0
PageRank 
References 
Authors
0.34
2
4
Name
Order
Citations
PageRank
Jiaxi Li120.71
Junfu Yin200.34
Yuk Ying Chung321125.47
Feng Sha4114.54