Inference of Poisson count processes using low-rank tensor data

Juan Andres Bazerque, Gonzalo Mateos, Georgios B Giannakis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

A novel regularizer capturing the tensor rank is introduced in this paper as the key enabler for completion of three-way data arrays with missing entries. The novel regularized imputation approach induces sparsity in the factors of the tensor's PARAFAC decomposition, thus reducing its rank. The focus is on count processes which emerge in diverse applications ranging from genomics to computer and social networking. Based on Poisson count data, a maximum aposteriori (MAP) estimator is developed using the Kullback-Leibler divergence criterion. This probabilistic approach also facilitates incorporation of correlated priors regularizing the rank, while endowing the tensor imputation method with extra smoothing and prediction capabilities. Tests on simulated and real datasets corroborate the sparsifying regularization effect, and demonstrate recovery of 15% missing RNA-sequencing data with an inference error of -12dB.

Original languageEnglish (US)
Title of host publication2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings
Pages5989-5993
Number of pages5
DOIs
StatePublished - Oct 18 2013
Event2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Vancouver, BC, Canada
Duration: May 26 2013May 31 2013

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
CountryCanada
CityVancouver, BC
Period5/26/135/31/13

Keywords

  • Poisson processes
  • Tensor
  • low-rank
  • missing data

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