Macau: Scalable Bayesian factorization with high-dimensional side information using MCMC

IEEE International Workshop on Machine Learning for Signal Processing, MLSP

Volumn 2017-September, Issue , 2017, Pages 1-6

  Simm, J ^a   Arany, A ^a   Zakeri, P ^a   Haber, T ^b   Wegner, J K ^c   Chupakhin, V ^c   Ceulemans, H ^c   Moreau, Y ^a  

^a UNIVERSITY OF LEUVEN   (Belgium)

^b HASSELT UNIVERSITY   (Belgium)

^c JANSSEN RESEARCH FOUNDATION   (Belgium)

Author keywords

High scale machine learning; Matrix factorization; MCMC; Side information

Indexed keywords

ARTIFICIAL INTELLIGENCE; FACTORIZATION; LEARNING SYSTEMS; SIGNAL PROCESSING;

BAYESIAN MATRIX FACTORIZATIONS; BLOCK CONJUGATE GRADIENTS; DRUG-PROTEIN INTERACTIONS; KRYLOV SUBSPACE METHOD; LINEAR COMPLEXITY; MATRIX FACTORIZATIONS; MCMC; SIDE INFORMATION;

MATRIX ALGEBRA;

EID: 85042254356     PISSN: 21610363     EISSN: 21610371     Source Type: Conference Proceeding    
DOI: 10.1109/MLSP.2017.8168143     Document Type: Conference Paper

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