Nonparametric bayesian matrix completion

Mingyuan Zhou; Chunping Wang; Minhua Chen; John Paisley; David Dunson; Lawrence Carin

doi:10.1109/SAM.2010.5606741

Nonparametric bayesian matrix completion

Mingyuan Zhou, Chunping Wang, Minhua Chen, John Paisley, David Dunson, Lawrence Carin

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

28 Scopus citations

Abstract

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
Pages	213-216
Number of pages	4
DOIs	https://doi.org/10.1109/SAM.2010.5606741
State	Published - Dec 20 2010
Externally published	Yes

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10.1109/SAM.2010.5606741

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title = "Nonparametric bayesian matrix completion",

abstract = "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. {\textcopyright} 2010 IEEE.",

author = "Mingyuan Zhou and Chunping Wang and Minhua Chen and John Paisley and David Dunson and Lawrence Carin",

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AU - Zhou, Mingyuan

AU - Wang, Chunping

AU - Chen, Minhua

AU - Paisley, John

AU - Dunson, David

AU - Carin, Lawrence

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PY - 2010/12/20

Y1 - 2010/12/20

N2 - 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.

AB - 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.

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M3 - Conference contribution

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SP - 213

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BT - 2010 IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2010

ER -

Nonparametric bayesian matrix completion

Abstract

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