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Variational Beta Process Hidden Markov Models with Shared Hidden States for Trajectory Recognition.

Jing Zhao1, Yi Zhang1, Shiliang Sun1

  • 1School of Computer Science and Technology, East China Normal University, Shanghai 200062, China.

Entropy (Basel, Switzerland)
|October 23, 2021
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Summary
This summary is machine-generated.

A new variational Beta process Hidden Markov Model (BP-HMM) improves trajectory recognition by enabling shared hidden states across classes and efficient inference. This model offers superior performance compared to existing BP-HMMs.

Keywords:
Beta processhidden Markov modelstrajectory recognitionvariational inference

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Area of Science:

  • Machine Learning
  • Pattern Recognition
  • Data Science

Background:

  • Hidden Markov Models (HMMs) are crucial for trajectory recognition.
  • Determining the number of hidden states in HMMs is challenging.
  • Nonparametric methods like Beta process HMMs (BP-HMMs) automate state determination and model shared information.

Purpose of the Study:

  • To develop an efficient nonparametric sequential model for trajectory recognition.
  • To capture cross-class shared information effectively.
  • To address the limitations of existing BP-HMMs regarding state transition matrices and inference speed.

Main Methods:

  • Proposed a novel variational BP-HMM model where hidden states are shared across classes.
  • Each class utilizes its own hidden states with a unified transition probability matrix.
  • Derived a more efficient variational inference method compared to sampling-based approaches.

Main Results:

  • The variational BP-HMM demonstrated superior performance in trajectory recognition.
  • Achieved better results than sampled BP-HMM and other related models on synthetic and real-world datasets.
  • The proposed model offers improved efficiency due to its variational inference method.

Conclusions:

  • The variational BP-HMM is an effective nonparametric sequential model for trajectory recognition.
  • It successfully models shared information across classes while maintaining computational efficiency.
  • This model presents a significant advancement over existing BP-HMMs for trajectory analysis.