Skip to main content

Multi-task learning for sequential data via iHMMs and the nested Dirichlet process

Publication ,  Journal Article
Ni, K; Carin, L; Dunson, D
Published in: ACM International Conference Proceeding Series
August 23, 2007

A new hierarchical nonparametric Bayesian model is proposed for the problem of multitask learning (MTL) with sequential data. Sequential data are typically modeled with a hidden Markov model (HMM), for which one often must choose an appropriate model structure (number of states) before learning. Here we model sequential data from each task with an infinite hidden Markov model (iHMM), avoiding the problem of model selection. The MTL for iHMMs is implemented by imposing a nested Dirichlet process (nDP) prior on the base distributions of the iHMMs. The nDP-iHMM MTL method allows us to perform task-level clustering and data-level clustering simultaneously, with which the learning for individual iHMMs is enhanced and between-task similarities are learned. Learning and inference for the nDP-iHMM MTL are based on a Gibbs sampler. The effectiveness of the framework is demonstrated using synthetic data as well as real music data.

Duke Scholars

Published In

ACM International Conference Proceeding Series

DOI

Publication Date

August 23, 2007

Volume

227

Start / End Page

689 / 696
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Ni, K., Carin, L., & Dunson, D. (2007). Multi-task learning for sequential data via iHMMs and the nested Dirichlet process. ACM International Conference Proceeding Series, 227, 689–696. https://doi.org/10.1145/1273496.1273583
Ni, K., L. Carin, and D. Dunson. “Multi-task learning for sequential data via iHMMs and the nested Dirichlet process.” ACM International Conference Proceeding Series 227 (August 23, 2007): 689–96. https://doi.org/10.1145/1273496.1273583.
Ni K, Carin L, Dunson D. Multi-task learning for sequential data via iHMMs and the nested Dirichlet process. ACM International Conference Proceeding Series. 2007 Aug 23;227:689–96.
Ni, K., et al. “Multi-task learning for sequential data via iHMMs and the nested Dirichlet process.” ACM International Conference Proceeding Series, vol. 227, Aug. 2007, pp. 689–96. Scopus, doi:10.1145/1273496.1273583.
Ni K, Carin L, Dunson D. Multi-task learning for sequential data via iHMMs and the nested Dirichlet process. ACM International Conference Proceeding Series. 2007 Aug 23;227:689–696.

Published In

ACM International Conference Proceeding Series

DOI

Publication Date

August 23, 2007

Volume

227

Start / End Page

689 / 696