Nonparametric bayesian matrix completion

Journal Article

The Beta-Binomial processes are considered for inferring missing values in matrices. The model moves beyond the low-rank assumption, modeling the matrix columns as residing in a nonlinear subspace. Large-scale problems are considered via efficient Gibbs sampling, yielding predictions as well as a measure of confidence in each prediction. Algorithm performance is considered for several datasets, with encouraging performance relative to existing approaches. © 2010 IEEE.

Full Text

Duke Authors

Cited Authors

  • Zhou, M; Wang, C; Chen, M; Paisley, J; Dunson, D; Carin, L

Published Date

  • December 20, 2010

Published In

  • 2010 Ieee Sensor Array and Multichannel Signal Processing Workshop, Sam 2010

Start / End Page

  • 213 - 216

Digital Object Identifier (DOI)

  • 10.1109/SAM.2010.5606741

Citation Source

  • Scopus