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Author Mingyuan Zhou ♦ Chunping Wang ♦ Minhua Chen ♦ Paisley, J. ♦ Dunson, D. ♦ Carin, L.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2010
Language English
Subject Domain (in DDC) Technology ♦ Engineering & allied operations ♦ Applied physics
Subject Keyword Motion pictures ♦ Collaboration ♦ Mathematical model ♦ Bayesian methods ♦ Strontium ♦ Sparse matrices ♦ Dictionaries
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.
Description Author affiliation: Electrical and Computer Engineering Department, Duke University Durham, NC 27708-0291 (Mingyuan Zhou; Chunping Wang; Minhua Chen; Paisley, J.) || Statistics Department, Duke University Durham, NC 27708-0291 (Dunson, D.; Carin, L.)
ISBN 9781424489787
ISSN 2151870X
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2010-10-04
Publisher Place Israel
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
e-ISBN 9781424489770
Size (in Bytes) 354.62 kB
Page Count 4
Starting Page 213
Ending Page 216


Source: IEEE Xplore Digital Library