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Bayesian time-aligned factor analysis of paired multivariate time series.

Publication ,  Journal Article
Roy, A; Borg, JS; Dunson, DB
Published in: Journal of machine learning research : JMLR
January 2021

Many modern data sets require inference methods that can estimate the shared and individual-specific components of variability in collections of matrices that change over time. Promising methods have been developed to analyze these types of data in static cases, but only a few approaches are available for dynamic settings. To address this gap, we consider novel models and inference methods for pairs of matrices in which the columns correspond to multivariate observations at different time points. In order to characterize common and individual features, we propose a Bayesian dynamic factor modeling framework called Time Aligned Common and Individual Factor Analysis (TACIFA) that includes uncertainty in time alignment through an unknown warping function. We provide theoretical support for the proposed model, showing identifiability and posterior concentration. The structure enables efficient computation through a Hamiltonian Monte Carlo (HMC) algorithm. We show excellent performance in simulations, and illustrate the method through application to a social mimicry experiment.

Duke Scholars

Published In

Journal of machine learning research : JMLR

EISSN

1533-7928

ISSN

1532-4435

Publication Date

January 2021

Volume

22

Start / End Page

250

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 4905 Statistics
  • 4611 Machine learning
  • 17 Psychology and Cognitive Sciences
  • 08 Information and Computing Sciences
 

Citation

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Chicago
ICMJE
MLA
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Roy, A., Borg, J. S., & Dunson, D. B. (2021). Bayesian time-aligned factor analysis of paired multivariate time series. Journal of Machine Learning Research : JMLR, 22, 250.
Roy, Arkaprava, Jana Schaich Borg, and David B. Dunson. “Bayesian time-aligned factor analysis of paired multivariate time series.Journal of Machine Learning Research : JMLR 22 (January 2021): 250.
Roy A, Borg JS, Dunson DB. Bayesian time-aligned factor analysis of paired multivariate time series. Journal of machine learning research : JMLR. 2021 Jan;22:250.
Roy, Arkaprava, et al. “Bayesian time-aligned factor analysis of paired multivariate time series.Journal of Machine Learning Research : JMLR, vol. 22, Jan. 2021, p. 250.
Roy A, Borg JS, Dunson DB. Bayesian time-aligned factor analysis of paired multivariate time series. Journal of machine learning research : JMLR. 2021 Jan;22:250.

Published In

Journal of machine learning research : JMLR

EISSN

1533-7928

ISSN

1532-4435

Publication Date

January 2021

Volume

22

Start / End Page

250

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 4905 Statistics
  • 4611 Machine learning
  • 17 Psychology and Cognitive Sciences
  • 08 Information and Computing Sciences