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Volumn , Issue , 2010, Pages 905-909

Infinite non-negative matrix factorization

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL COSTS; GIBBS SAMPLING; MODEL ORDER; NONNEGATIVE MATRIX FACTORIZATION; NUMBER OF COMPONENTS; REVERSIBLE JUMP MARKOV CHAIN MONTE CARLO; SYNTHETIC AND REAL DATA; THERMODYNAMIC INTEGRATION;

EID: 84863798093     PISSN: 22195491     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (12)

References (34)
  • 2
    • 0041974049 scopus 로고
    • Marginal likelihood from the Gibbs output
    • Dec
    • S. Chib. Marginal likelihood from the Gibbs output. Journal of the American Statistical Association, 90(432):1313 1321, Dec 1995.
    • (1995) Journal of the American Statistical Association , vol.90 , Issue.432 , pp. 1313-1321
    • Chib, S.1
  • 4
    • 42149143421 scopus 로고    scopus 로고
    • Marginal likelihood estimation via power posteriors
    • N. Friel and A. N. Pettitt. Marginal likelihood estimation via power posteriors.© EURASIP, 2010.Journal of the Royal Statistical Society, 70(3):589 607, 2008.
    • (2008) Journal of the Royal Statistical Society , vol.70 , Issue.3 , pp. 589-607
    • Friel, N.1    Pettitt, A.N.2
  • 6
    • 0000736067 scopus 로고    scopus 로고
    • Simulating normalizing constants: From importance sampling to bridge sampling to path sampling
    • A. Gelman and X.-L. Meng. Simulating normalizing constants: From importance sampling to bridge sampling to path sampling. Statistical Science, 13(5): 163 185, 1998.
    • (1998) Statistical Science , vol.13 , Issue.5 , pp. 163-185
    • Gelman, A.1    Meng, X.-L.2
  • 8
    • 77956889087 scopus 로고
    • Reversible jump markov chain monte carlo computation and bayesian model determination
    • P. J. Green. Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrica, 82(4):711 732, 1995.".
    • (1995) Biometrica , vol.82 , Issue.4 , pp. 711-732
    • Green, P.J.1
  • 11
    • 1842486852 scopus 로고    scopus 로고
    • A split-merge markov chain monte carlo procedure for the dirichlet process mixture model
    • S. Iain and R. M. Neal. A split-merge Markov chain Monte Carlo procedure for the Dirichlet process mixture model. Journal of Computational and Graphical Statistics, 13(1):158 182, 2004.
    • (2004) Journal of Computational and Graphical Statistics , vol.13 , Issue.1 , pp. 158-182
    • Iain, S.1    Neal, R.M.2
  • 12
    • 70350175761 scopus 로고    scopus 로고
    • Fast projection-based methods for the least squares nonnegative matrix approximation problem
    • D. Kim, S. Sra, and S. Dhillon. Fast projection-based methods for the least squares nonnegative matrix approximation problem. Statistical Analysis and Data Mining, 1:38 51, 2008.
    • (2008) Statistical Analysis and Data Mining , vol.1 , pp. 38-51
    • Kim, D.1    Sra, S.2    Dhillon, S.3
  • 13
    • 67349093319 scopus 로고    scopus 로고
    • Nonnegative matrix factorization based on alternating nonnegativity constrained least squares and active set method
    • H. Kim and H. Park. Nonnegative matrix factorization based on alternating nonnegativity constrained least squares and active set method. Matrix Analysis and Applications, S1AM Journal on, 30(2):713 730, 2008.
    • (2008) Matrix Analysis and Applications, S1AM Journal on , vol.30 , Issue.2 , pp. 713-730
    • Kim, H.1    Park, H.2
  • 14
    • 38149041041 scopus 로고    scopus 로고
    • Inlinite sparse factor analysis and inlinite independent components analysis
    • volume 4666 of Lecture Notes in Computer Science Series (LNCS), Springer
    • D. Knowles and Z. Ghahramani. Inlinite sparse factor analysis and inlinite independent components analysis. In Independent Component Analysis and Signal Separation, International Conference on (1CA), volume 4666 of Lecture Notes in Computer Science Series (LNCS), pages 381 388. Springer, 2007.
    • (2007) Independent Component Analysis and Signal Separation, International Conference on (1CA) , pp. 381-388
    • Knowles, D.1    Ghahramani, Z.2
  • 16
    • 0033592606 scopus 로고    scopus 로고
    • Learning the parts of objects by non-negative matrix factorization
    • D. D. Lee and H. S. Seung. Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755):788 791, 1999.
    • (1999) Nature , vol.401 , Issue.6755 , pp. 788-791
    • Lee, D.D.1    Seung, H.S.2
  • 18
    • 35548969471 scopus 로고    scopus 로고
    • Projcctcd gradient methods for non-negative matrix factorization
    • C.-J. Lin. Projcctcd gradient methods for non-negative matrix factorization. Neural Computation, 19:2756 2779, 2007.
    • (2007) Neural Computation , vol.19 , pp. 2756-2779
    • Lin, C.-J.1
  • 19
    • 1842539381 scopus 로고    scopus 로고
    • Bayesian model assessment in factor analysis
    • H. F. Lopes and M. West. Bayesian model assessment in factor analysis. Statistica Sinica, 14:41 67, 2004.
    • (2004) Statistica Sinica , vol.14 , pp. 41-67
    • Lopes, H.F.1    West, M.2    Lin, C.-.J.3
  • 22
    • 33947514540 scopus 로고    scopus 로고
    • ERPWAVE- LAB a toolbox for multi-channel analysis of time-frequency transformed event related potentials
    • M. Mprup, L. K. Hansen, and S. M. Arnfred. ERPWAVE- LAB a toolbox for multi-channel analysis of time-frequency transformed event related potentials.© EURASIP, 2010.Journal of Neuro- science Methods, 161: (361 368), 2007.
    • (2007) Journal of Neuro- Science Methods , vol.161 , Issue.361-368
    • Mprup, M.1    Hansen, L.K.2    Arnfred, S.M.3
  • 23
    • 33750402597 scopus 로고    scopus 로고
    • Separation of non-negative mixture of non- negative sources using a Bayesian approach and MCMC sampling
    • Nov
    • S. Moussaoui, D. Brie, A. Mohammad-Djafari, and G. Carteret. Separation of non-negative mixture of non- negative sources using a Bayesian approach and MCMC sampling. Signal Processing, IEEE'lYansactions on, 54(11):4133 4145, Nov 2006.
    • (2006) Signal Processing, IEEE'LYansactions on , vol.54 , Issue.11 , pp. 4133-4145
    • Moussaoui, S.1    Brie, D.2    Mohammad-Djafari, A.3    Carteret, G.4
  • 25
    • 0028561099 scopus 로고
    • Positive matrix factorization: A nonnegative factor model with optimal utilization of error- estimates of data values
    • Jun
    • P. Paatero and U. Tapper. Positive matrix factorization: A nonnegative factor model with optimal utilization of error- estimates of data values. Enuironmetries, 5(2):111-126,© EURASIP, 2010.Jun 1994.
    • (1994) Enuironmetries , vol.5 , Issue.2 , pp. 111-126
    • Paatero, P.1    Tapper, U.2
  • 26
    • 33646682646 scopus 로고    scopus 로고
    • Nonnegative matrix factorization for spectral data analysis
    • Jul
    • V. P. Pauca, J. Piper, and R. J. Plemmons. Nonnegative matrix factorization for spectral data analysis. Linear Algebra and its Applications, 416(1):29 47, Jul 2006.
    • (2006) Linear Algebra and Its Applications , vol.416 , Issue.1 , pp. 29-47
    • Pauca, V.P.1    Piper, J.2    Plemmons, R.J.3
  • 27
    • 10044269618 scopus 로고    scopus 로고
    • Nonnegative matrix factorization for rapid recovery of constituent spectra in magnetic reso-nance chemical shift imaging of the brain
    • Dec
    • P. Sajda, S. Du, T. R. Brown, R. Stoyanova, D. C. Shungu, X. Mao, and L. C. Parra. Nonnegative matrix factorization for rapid recovery of constituent spectra in magnetic resonance chemical shift imaging of the brain. Medical Imaging, IEEE Transactions on, 23(12):1453 1465, Dec 2004.
    • (2004) Medical Imaging, IEEE Transactions on , vol.23 , Issue.12 , pp. 1453-1465
    • Sajda, P.1    Du, S.2    Brown, T.R.3    Stoyanova, R.4    Shungu, D.C.5    Mao, X.6    Parra, L.C.7
  • 30
    • 84858072149 scopus 로고    scopus 로고
    • Probabilistic non-negative tensor factorization using Markov chain Monte Carlo
    • M. N. Schmidt and S. Mohamed. Probabilistic non-negative tensor factorization using Markov chain Monte Carlo. European Signal Processing Conference (EUS1PCO), pages 1918 1922, 2009.
    • (2009) European Signal Processing Conference (EUS1PCO) , pp. 1918-1922
    • Schmidt, M.N.1    Mohamed, S.2
  • 31
    • 33745711487 scopus 로고    scopus 로고
    • Nonnegative matrix factor 2-d deconvolution for blind single channel source separation
    • volume 3889 of Lecture Notes in Computer Science (LNCS), Springer, Apr
    • M. N. Schmidt and M. Mprup. Nonnegative matrix factor 2-d deconvolution for blind single channel source separation. In Independent Component Analysis and Blind Signal Separation, International Conference on (1CA), volume 3889 of Lecture Notes in Computer Science (LNCS), pages 700 707. Springer, Apr 2006.
    • (2006) Independent Component Analysis and Blind Signal Separation, International Conference on (1CA) , pp. 700-707
    • Schmidt, M.N.1    Mprup, M.2


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