The Recurrent Sticky Hierarchical Dirichlet Process Hidden Markov Model
| Title: | The Recurrent Sticky Hierarchical Dirichlet Process Hidden Markov Model |
|---|---|
| Authors: | Słupiński, Mikołaj, Lipiński, Piotr |
| Publication Year: | 2024 |
| Collection: | Computer Science Mathematics Statistics |
| Subject Terms: | Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Mathematics - Dynamical Systems, Statistics - Machine Learning |
| More Details: | The Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) is a natural Bayesian nonparametric extension of the classical Hidden Markov Model for learning from (spatio-)temporal data. A sticky HDP-HMM has been proposed to strengthen the self-persistence probability in the HDP-HMM. Then, disentangled sticky HDP-HMM has been proposed to disentangle the strength of the self-persistence prior and transition prior. However, the sticky HDP-HMM assumes that the self-persistence probability is stationary, limiting its expressiveness. Here, we build on previous work on sticky HDP-HMM and disentangled sticky HDP-HMM, developing a more general model: the recurrent sticky HDP-HMM (RS-HDP-HMM). We develop a novel Gibbs sampling strategy for efficient inference in this model. We show that RS-HDP-HMM outperforms disentangled sticky HDP-HMM, sticky HDP-HMM, and HDP-HMM in both synthetic and real data segmentation. |
| Document Type: | Working Paper |
| Access URL: | http://arxiv.org/abs/2411.04278 |
| Accession Number: | edsarx.2411.04278 |
| Database: | arXiv |
| Description not available. |