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Volumn 22, Issue , 2012, Pages 1012-1018

Fast variational Bayesian inference for non-conjugate matrix factorization models

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; BAYESIAN NETWORKS; FACTORIZATION; INFERENCE ENGINES; MAXIMUM LIKELIHOOD ESTIMATION; SHRINKAGE; STOCHASTIC SYSTEMS; VARIATIONAL TECHNIQUES;

EID: 84919880360     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper
Times cited : (31)

References (19)
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  • 2
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    • O. Chapelle and Z. Harchaoui. A machine-learning approach to conjoint analysis. In NIPS 17, pages 257-264, 2005.
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  • 5
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    • Variational bounds for mixed-data factor analysis
    • E. Khan, B. Marlin, G. Bouchard, and K. Murphy. Variational bounds for mixed-data factor analysis. In NIPS 22, 2010.
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    • Khan, E.1    Marlin, B.2    Bouchard, G.3    Murphy, K.4
  • 9
    • 80555154425 scopus 로고    scopus 로고
    • Theoretical analysis of Bayesian matrix factorization
    • S. Nakajima and Sugiyama. Theoretical analysis of Bayesian matrix factorization. JMLR, 12:2579-2644, 2011.
    • (2011) JMLR , vol.12 , pp. 2579-2644
    • Nakajima, S.1    Sugiyama2
  • 10
    • 9744239998 scopus 로고    scopus 로고
    • Maximum likelihood estimation of a stochastic integrate-and-fire neural encoding model
    • L. Paninski, J. Pillow, and E. Simoncelli. Maximum likelihood estimation of a stochastic integrate-and-fire neural encoding model. N. Comp., 16:2533-2561, 2004.
    • (2004) N. Comp. , vol.16 , pp. 2533-2561
    • Paninski, L.1    Pillow, J.2    Simoncelli, E.3
  • 11
    • 65449137417 scopus 로고    scopus 로고
    • Principal component analysis for large scale problems with lots of missing values
    • T. Raiko, A. Ilin, and J. Karhunen. Principal component analysis for large scale problems with lots of missing values. In ECML 18, pages 691-698, 2007.
    • (2007) ECML , vol.18 , pp. 691-698
    • Raiko, T.1    Ilin, A.2    Karhunen, J.3
  • 12
    • 74349093161 scopus 로고    scopus 로고
    • Inference algorithms and learning theory for Bayesian sparse factor analysis
    • M. Rattray, O. Stegle, K. Sharp, and J. Winn. Inference algorithms and learning theory for Bayesian sparse factor analysis. Journal of Physics: Conference Series, 197(012002), 2009.
    • (2009) Journal of Physics: Conference Series , vol.197 , pp. 012002
    • Rattray, M.1    Stegle, O.2    Sharp, K.3    Winn, J.4
  • 15
    • 85161989354 scopus 로고    scopus 로고
    • Probabilistic matrix factorization
    • R. Salakhutdinov and A. Mnih. Probabilistic matrix factorization. In NIPS 20, pages 1257-1264, 2008.
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    • Salakhutdinov, R.1    Mnih, A.2
  • 16
    • 84898932317 scopus 로고    scopus 로고
    • Maximum margin matrix factorization
    • N. Srebro, J. Rennie, and T. Jaakkola. Maximum margin matrix factorization. In NIPS 17, pages 1329-1336, 2005.
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    • Srebro, N.1    Rennie, J.2    Jaakkola, T.3
  • 17
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    • Recovering low-rank and sparse components of matrices from incomplete and noisy observations
    • M. Tao and X. Yuan. Recovering low-rank and sparse components of matrices from incomplete and noisy observations. SIAM J. Optim., 21(1):57-81, 2011.
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  • 19
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    • An efficient and general augmented Lagrangian algorithm for learning low-rank matrices
    • R. Tomioka, T. Suzuki, M. Sugiyama, and H. Kashima. An efficient and general augmented Lagrangian algorithm for learning low-rank matrices. In ICML 27, pages 1087-1094, 2010.
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    • Tomioka, R.1    Suzuki, T.2    Sugiyama, M.3    Kashima, H.4


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