Dynamic poisson factor analysis

Conference Paper

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.

Full Text

Duke Authors

Cited Authors

  • Zhang, Y; Zhao, Y; David, L; Henao, R; Carin, L

Published Date

  • January 31, 2017

Published In

Start / End Page

  • 1359 - 1364

International Standard Serial Number (ISSN)

  • 1550-4786

International Standard Book Number 13 (ISBN-13)

  • 9781509054725

Digital Object Identifier (DOI)

  • 10.1109/ICDM.2016.111

Citation Source

  • Scopus