Details

Autor(en) / Beteiligte

• Lan, Hui
• Liu, Ziquan
• Hsiao, Janet H.
• Yu, Dan
• Chan, Antoni B.

Titel

Clustering Hidden Markov Models With Variational Bayesian Hierarchical EM

Ist Teil von

• IEEE transaction on neural networks and learning systems, 2023-03, Vol.34 (3), p.1537-1551

Ort / Verlag

United States: IEEE

Erscheinungsjahr

2023

Quelle

IEEE/IET Electronic Library (IEL)

Beschreibungen/Notizen

• The hidden Markov model (HMM) is a broadly applied generative model for representing time-series data, and clustering HMMs attract increased interest from machine learning researchers. However, the number of clusters (<inline-formula> <tex-math notation="LaTeX">K </tex-math></inline-formula>) and the number of hidden states (<inline-formula> <tex-math notation="LaTeX">S </tex-math></inline-formula>) for cluster centers are still difficult to determine. In this article, we propose a novel HMM-based clustering algorithm, the variational Bayesian hierarchical EM algorithm, which clusters HMMs through their densities and priors and simultaneously learns posteriors for the novel HMM cluster centers that compactly represent the structure of each cluster. The numbers <inline-formula> <tex-math notation="LaTeX">K </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">S </tex-math></inline-formula> are automatically determined in two ways. First, we place a prior on the pair <inline-formula> <tex-math notation="LaTeX">(K,S) </tex-math></inline-formula> and approximate their posterior probabilities, from which the values with the maximum posterior are selected. Second, some clusters and states are pruned out implicitly when no data samples are assigned to them, thereby leading to automatic selection of the model complexity. Experiments on synthetic and real data demonstrate that our algorithm performs better than using model selection techniques with maximum likelihood estimation.