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