Nonparametric Bayesian matrix completion
Mingyuan Zhou, Chunping Wang, Minhua Chen, John William Paisley, David B. Dunson, Lawrence Carin · 2010
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.