Date of this Version
Proceedings of the 25th International Conference on Machine Learning
The hierarchical Dirichlet process hidden Markov model (HDP-HMM) is a flexible, nonparametric model which allows state spaces of unknown size to be learned from data. We demonstrate some limitations of the original HDP-HMM formulation (Teh et al., 2006), and propose a sticky extension which allows more robust learning of smoothly varying dynamics. Using DP mixtures, this formulation also allows learning of more complex, multimodal emission distributions. We further develop a sampling algorithm that employs a truncated approximation of the DP to jointly resample the full state sequence, greatly improving mixing rates. Via extensive experiments with synthetic data and the NIST speaker diarization database, we demonstrate the advantages of our sticky extension, and the utility of the HDP-HMM in real-world applications.
Fox, E. B., Sudderth, E. B., Jordan, M. I., & Willsky, A. S. (2008). An HDP-HMM for Systems With State Persistence. Proceedings of the 25th International Conference on Machine Learning, 312-319. Retrieved from https://repository.upenn.edu/statistics_papers/116
Date Posted: 27 November 2017