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.
|Original language||English (US)|
|Title of host publication||2010 IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2010|
|Number of pages||4|
|State||Published - Dec 20 2010|