An HDP-HMM for systems with state persistence

Proceedings of the 25th International Conference on Machine Learning

Volumn , Issue , 2008, Pages 312-319

  Fox, Emily B ^a   Sudderth, Erik B ^b   Jordan, Michael I ^b   Willsky, Alan S ^a  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION ALGORITHMS; MACHINE LEARNING; DYNAMIC POSITIONING; EDUCATION; LEARNING ALGORITHMS; LEARNING SYSTEMS; MARKOV PROCESSES; PERCOLATION (SOLID STATE); ROBOT LEARNING;

EMISSION DISTRIBUTION; HIERARCHICAL DIRICHLET PROCESS HIDDEN MARKOV MODEL (HDP HMM); NON-PARAMETRIC MODEL; ROBUST LEARNING; SAMPLING ALGORITHM; SPEAKER DIARIZATION; STATE SEQUENCES; SYNTHETIC DATA; DIRICHLET PROCESS; EMISSION DISTRIBUTIONS; MARKOV MODELS; MIXING RATES; MULTI MODALS; NON-PARAMETRIC MODELS; REAL-WORLD APPLICATIONS; RESAMPLE; SAMPLING ALGORITHMS; STATE SPACES; SYNTHETIC DATUM;

HIDDEN MARKOV MODELS;

EID: 56449084167     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1390156.1390196     Document Type: Conference Paper

References (12)

1. Beal, M. J., Ghahramani, Z., & Rasmussen, C. E. (2002). The infinite hidden Markov model. NIPS (pp. 577-584).
2. Fox, E., Sudderth, E., Jordan, M., & Willsky, A. (2007). A tempered HDP-HMM for systems with state persistence. MIT LIDS, TR #2777.
3. Ishwaran, H., & Zarepour, M. (2002). Exact and approximate sum-representations for the Dirichlet process. Can. J. Stat., 30, 269-283.
4. Kivinen, J. J., Sudderth, E. B., & Jordan, M. I. (2007). Learning multiscale representations of natural scenes using Dirichlet processes. ICCV (pp. 1-8).
5. NIST (2007). Rich transcriptions database. http://www.nist.gov/speech/ tests/rt/.
6. Rabiner, L. (1989). A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE, 77, 257-286.
7. Rodriguez, A., Dunson, D., & Gelfand, A. (2006). The nested Dirichlet process. Duke ISDS, TR #06-19.
8. Scott, S. (2002). Bayesian methods for hidden Markov models: Recursive computing in the 21st century. J. Amer. Stat. Assoc., 97, 337-351.
9. Sethuraman, J. (1994). A constructive definition of Dirichlet priors. Stat. Sinica, 4, 639-650.
10. Teh, Y. W., Jordan, M. I., Beal, M. J., & Blei, D. M. (2006). Hierarchical Dirichlet processes. J. Amer. Stat. Assoc., 101, 1566-1581.
11. Wooters, C., & Huijbregts, M. (2007). The ICSI RT07s speaker diarization system. To appear in LNCS.
12. Xing, E., & Sohn, K.-A. (2007). Hidden Markov Dirichlet process: Modeling genetic inference in open ancestral space. Bayes. Analysis, 2, 501-528.