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Dynamic poisson factor analysis

Publication ,  Conference
Zhang, Y; Zhao, Y; David, L; Henao, R; Carin, L
Published in: Proceedings - IEEE International Conference on Data Mining, ICDM
July 2, 2016

We introduce a novel dynamic model for discrete time-series data, in which the temporal sampling may be nonuniform. The model is specified by constructing a hierarchy of Poisson factor analysis blocks, one for the transitions between latent states and the other for the emissions between latent states and observations. Latent variables are binary and linked to Poisson factor analysis via Bernoulli-Poisson specifications. The model is derived for count data but can be readily modified for binary observations. We derive efficient inference via Markov chain Monte Carlo, that scales with the number of non-zeros in the data and latent binary states, yielding significant acceleration compared to related models. Experimental results on benchmark data show the proposed model achieves state-of-The-Art predictive performance. Additional experiments on microbiome data demonstrate applicability of the proposed model to interesting problems in computational biology where interpretability is of utmost importance.

Duke Scholars

Published In

Proceedings - IEEE International Conference on Data Mining, ICDM

DOI

ISSN

1550-4786

ISBN

9781509054725

Publication Date

July 2, 2016

Volume

0

Start / End Page

1359 / 1364
 

Citation

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Zhang, Y., Zhao, Y., David, L., Henao, R., & Carin, L. (2016). Dynamic poisson factor analysis. In Proceedings - IEEE International Conference on Data Mining, ICDM (Vol. 0, pp. 1359–1364). https://doi.org/10.1109/ICDM.2016.111
Zhang, Y., Y. Zhao, L. David, R. Henao, and L. Carin. “Dynamic poisson factor analysis.” In Proceedings - IEEE International Conference on Data Mining, ICDM, 0:1359–64, 2016. https://doi.org/10.1109/ICDM.2016.111.
Zhang Y, Zhao Y, David L, Henao R, Carin L. Dynamic poisson factor analysis. In: Proceedings - IEEE International Conference on Data Mining, ICDM. 2016. p. 1359–64.
Zhang, Y., et al. “Dynamic poisson factor analysis.” Proceedings - IEEE International Conference on Data Mining, ICDM, vol. 0, 2016, pp. 1359–64. Scopus, doi:10.1109/ICDM.2016.111.
Zhang Y, Zhao Y, David L, Henao R, Carin L. Dynamic poisson factor analysis. Proceedings - IEEE International Conference on Data Mining, ICDM. 2016. p. 1359–1364.

Published In

Proceedings - IEEE International Conference on Data Mining, ICDM

DOI

ISSN

1550-4786

ISBN

9781509054725

Publication Date

July 2, 2016

Volume

0

Start / End Page

1359 / 1364