메뉴 건너뛰기




Volumn 17, Issue 5, 2007, Pages 891-913

Variational and stochastic inference for Bayesian source separation

Author keywords

Gibbs sampler; Markov chain Monte Carlo; Source separation; Variational Bayes

Indexed keywords

COMPUTATIONAL METHODS; COMPUTER SIMULATION; GAUSSIAN NOISE (ELECTRONIC); MARKOV PROCESSES; MESSAGE PASSING; MONTE CARLO METHODS; STOCHASTIC MODELS;

EID: 34547454757     PISSN: 10512004     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.dsp.2007.03.008     Document Type: Article
Times cited : (51)

References (49)
  • 1
    • 0031288562 scopus 로고    scopus 로고
    • A. Mohammad-Djafari, A Bayesian estimation method for detection, localisation and estimation of superposed sources in remote sensing, in: SPIE'97, San Diego, July 1997
  • 2
    • 34547464517 scopus 로고    scopus 로고
    • A. Mohammad-Djafari, A Bayesian approach to source separation, in: Proc. 19th International Workshop on Bayesian Inference and Maximum Entropy Methods (MaxEnt99), Boise, USA, August 1999
  • 3
    • 57649118678 scopus 로고    scopus 로고
    • K.H. Knuth, Bayesian source separation and localization, in: SPIE'98: Bayesian Inference for Inverse Problems, San Diego, July 1998, pp. 147-158
  • 4
    • 34547420030 scopus 로고    scopus 로고
    • K.H. Knuth, A Bayesian approach to source separation, in: Proc. 1st International Workshop on Independent Component Analysis and Signal Separation, Aussois, France, January 1999, pp. 283-288
  • 5
    • 34547442273 scopus 로고    scopus 로고
    • K.H. Knuth, H.G. Vaughan, Convergent Bayesian formulations of blind source separation and electromagnetic source estimation, in: Maximum Entropy and Bayesian Methods (MaxEnt), Munich, 1998, pp. 217-226
  • 6
    • 22944434926 scopus 로고    scopus 로고
    • A Bayesian approach to blind source separation
    • Rowe D.B. A Bayesian approach to blind source separation. J. Interdisciplin. Math. 5 1 (2002) 49-76
    • (2002) J. Interdisciplin. Math. , vol.5 , Issue.1 , pp. 49-76
    • Rowe, D.B.1
  • 9
    • 0002741125 scopus 로고    scopus 로고
    • Ensemble learning for blind source separation
    • Roberts S.J., and Everson R.M. (Eds), Cambridge Univ. Press, Cambridge
    • Miskin J., and Mackay D. Ensemble learning for blind source separation. In: Roberts S.J., and Everson R.M. (Eds). Independent Component Analysis (2001), Cambridge Univ. Press, Cambridge 209-233
    • (2001) Independent Component Analysis , pp. 209-233
    • Miskin, J.1    Mackay, D.2
  • 12
    • 35048903602 scopus 로고    scopus 로고
    • C. Févotte, S.J. Godsill, P.J. Wolfe, Bayesian approach for blind separation of underdetermined mixtures of sparse sources, in: Proc. 5th International Conference on Independent Component Analysis and Blind Source Separation (ICA 2004), Granada, Spain, 2004, pp. 398-405
  • 13
    • 34547429873 scopus 로고    scopus 로고
    • C. Févotte, S.J. Godsill, A Bayesian approach for blind separation of sparse sources, IEEE Trans. Speech and Audio Processing, in press, available at http://persos.mist-technologies.com/~cfevotte/
  • 15
    • 0033692661 scopus 로고    scopus 로고
    • A. Jourjine, S. Rickard, O. Yilmaz, Blind separation of disjoint orthogonal signals: Demixing n sources from 2 mixtures, in: Proc. ICASSP-5, Istanbul, Turkey, June 2000, pp. 2985-2988
  • 16
    • 0001330172 scopus 로고    scopus 로고
    • Propagation algorithms for variational Bayesian learning
    • Ghahramani Z., and Beal M. Propagation algorithms for variational Bayesian learning. Neural Inform. Process. Syst. 13 (2000)
    • (2000) Neural Inform. Process. Syst. , vol.13
    • Ghahramani, Z.1    Beal, M.2
  • 17
    • 34547412386 scopus 로고    scopus 로고
    • M. Wainwright, M.I. Jordan, Graphical models, exponential families, and variational inference, Technical Report 649, Department of Statistics, UC Berkeley, September 2003
  • 19
    • 0033561886 scopus 로고    scopus 로고
    • Independent factor analysis
    • Attias H. Independent factor analysis. Neural Comput. 11 4 (1999) 803-851
    • (1999) Neural Comput. , vol.11 , Issue.4 , pp. 803-851
    • Attias, H.1
  • 20
    • 34547431437 scopus 로고    scopus 로고
    • H. Lappalainen, Ensemble learning for independent component analysis, in: Proceedings of Int. Workshop on Independent Component Analysis and Signal Separation (ICA'99), Aussois, France, 1999, pp. 7-12
  • 21
    • 34547420524 scopus 로고    scopus 로고
    • H. Valpola, Nonlinear independent component analysis using ensemble learning: Theory, in: Proceedings of the Second International Workshop on Independent Component Analysis and Blind Signal Separation, ICA 2000, Helsinki, Finland, 2000, pp. 251-256
  • 22
    • 0038132749 scopus 로고    scopus 로고
    • A variational method for learning sparse and overcomplete representations
    • Girolami M. A variational method for learning sparse and overcomplete representations. Neural Comput. 13 11 (2001) 2517-2532
    • (2001) Neural Comput. , vol.13 , Issue.11 , pp. 2517-2532
    • Girolami, M.1
  • 23
    • 0001387715 scopus 로고    scopus 로고
    • Mean-field approaches to independent component analysis
    • Hojen-Sorensen P., Winther O., and Hansen L.K. Mean-field approaches to independent component analysis. Neural Comput. 14 (2002) 889-918
    • (2002) Neural Comput. , vol.14 , pp. 889-918
    • Hojen-Sorensen, P.1    Winther, O.2    Hansen, L.K.3
  • 24
    • 0041324801 scopus 로고    scopus 로고
    • Variational Bayesian learning of ica with missing data
    • Chan K., Lee T.W., and Sejnowski T.J. Variational Bayesian learning of ica with missing data. Neural Comput. 15 (2003) 1991-2011
    • (2003) Neural Comput. , vol.15 , pp. 1991-2011
    • Chan, K.1    Lee, T.W.2    Sejnowski, T.J.3
  • 25
    • 34547409705 scopus 로고    scopus 로고
    • O. Winther, K.B. Petersen, Flexible and efficient implementations of Bayesian independent component analysis, Neurocomputing, 2006, submitted for publication
  • 27
    • 84863650825 scopus 로고    scopus 로고
    • A.T. Cemgil, C. Fevotte, S.J. Godsill, Blind separation of sparse sources using variational EM, in: 13th European Signal Processing Conference, Antalya, Turkey, 2005. EURASIP. URL http://www-sigproc.eng.cam.ac.uk/~cf269/eusipco05/sound_files.html
  • 28
    • 34547481747 scopus 로고    scopus 로고
    • S. Moussaoui, D. Brie, A. Mohammad-Djafari, C. Carteret, Separation of non-negative mixture of non-negative sources using a Bayesian approach and MCMC sampling, IEEE Trans. Signal Process., in press
  • 29
    • 0003860037 scopus 로고    scopus 로고
    • Gilks W.R., Richardson S., and Spiegelhalter D.J. (Eds), CRC Press, London
    • In: Gilks W.R., Richardson S., and Spiegelhalter D.J. (Eds). Markov Chain Monte Carlo in Practice (1996), CRC Press, London
    • (1996) Markov Chain Monte Carlo in Practice
  • 31
    • 77956890234 scopus 로고
    • Monte Carlo sampling methods using Markov chains and their applications
    • Hastings W.K. Monte Carlo sampling methods using Markov chains and their applications. Biometrika 57 (1970) 97-109
    • (1970) Biometrika , vol.57 , pp. 97-109
    • Hastings, W.K.1
  • 32
    • 0040944354 scopus 로고    scopus 로고
    • Markov chain Monte Carlo: Some practical implications of theoretical results
    • Roberts G.O., and Rosenthal J.S. Markov chain Monte Carlo: Some practical implications of theoretical results. Can. J. Statist. 26 (1998) 5-31
    • (1998) Can. J. Statist. , vol.26 , pp. 5-31
    • Roberts, G.O.1    Rosenthal, J.S.2
  • 33
    • 34547463449 scopus 로고    scopus 로고
    • W. Wiegerinck, Variational approximations between mean field theory and the junction tree algorithm, in: UAI (16th conference), 2000, pp. 626-633
  • 35
    • 0002788893 scopus 로고    scopus 로고
    • A view of the EM algorithm that justifies incremental, sparse, and other variants
    • MIT Press, Cambridge, MA. 0-262-60032-3
    • Neal R.M., and Hinton G.E. A view of the EM algorithm that justifies incremental, sparse, and other variants. Learning in Graphical Models (1999), MIT Press, Cambridge, MA. 0-262-60032-3 355-368
    • (1999) Learning in Graphical Models , pp. 355-368
    • Neal, R.M.1    Hinton, G.E.2
  • 36
  • 37
    • 25444480219 scopus 로고    scopus 로고
    • On the effect of the form of the posterior approximation in variational learning of ica models
    • Ilin A., and Valpola H. On the effect of the form of the posterior approximation in variational learning of ica models. Neural Process. Lett. 22 2 (2005)
    • (2005) Neural Process. Lett. , vol.22 , Issue.2
    • Ilin, A.1    Valpola, H.2
  • 38
    • 84899024135 scopus 로고    scopus 로고
    • D. Barber, W. Wiegerinck, Tractable variational structures for approximating graphical models, in: M. Kearns, S. Solla, D. Cohn (Eds.), Advances in Neural Information Processing Systems (NIPS), 1999, pp. 183-189
  • 41
    • 34547412385 scopus 로고    scopus 로고
    • L.K. Hansen, K.B. Petersen, Monaural ICA of white noise mixtures is hard, in: Proceedings of ICA 2003, pp. 815-820
  • 42
    • 22944474285 scopus 로고    scopus 로고
    • On the slow convergence of EM and VBEM in low noise linear mixtures
    • Petersen K.B., Winther O., and Hansen L.K. On the slow convergence of EM and VBEM in low noise linear mixtures. Neural Comput. 17 (2005) 1-6
    • (2005) Neural Comput. , vol.17 , pp. 1-6
    • Petersen, K.B.1    Winther, O.2    Hansen, L.K.3
  • 43
    • 0001692404 scopus 로고    scopus 로고
    • Suppressing random walks in Markov chain Monte Carlo using ordered overrelaxation
    • Jordan M.I. (Ed), Kluwer Academic, Dordrecht
    • Neal R.M. Suppressing random walks in Markov chain Monte Carlo using ordered overrelaxation. In: Jordan M.I. (Ed). Learning in Graphical Models (1998), Kluwer Academic, Dordrecht 205-225
    • (1998) Learning in Graphical Models , pp. 205-225
    • Neal, R.M.1
  • 44
    • 0001460136 scopus 로고    scopus 로고
    • On sequential Monte Carlo sampling methods for Bayesian filtering
    • Doucet A., Godsill S., and Andrieu C. On sequential Monte Carlo sampling methods for Bayesian filtering. Statist. Comput. 10 3 (2000) 197-208
    • (2000) Statist. Comput. , vol.10 , Issue.3 , pp. 197-208
    • Doucet, A.1    Godsill, S.2    Andrieu, C.3
  • 45
    • 17744411678 scopus 로고    scopus 로고
    • E. Sudderth, A. Ihler, W. Freeman, A. Willsky, Nonparametric belief propagation, in: Proceedings of IEEE Computer Vision and Pattern Recognition Conference (CVPR), 2003
  • 46
    • 48049094154 scopus 로고    scopus 로고
    • M. Briers, A. Doucet, S.S. Singh, K. Weekes, Particle filters for graphical models, in: Proceedings of Nonlinear Statistical Signal Processing Workshop. IEEE, 2006
  • 47
    • 34547481253 scopus 로고    scopus 로고
    • T. Minka, Divergence measures and message passing. Technical Report MSR-TR-2005-173, Microsoft Research, Cambridge, 2005
  • 48
    • 29244438430 scopus 로고    scopus 로고
    • Expectation consistent approximate inference
    • Opper M., and Winther O. Expectation consistent approximate inference. J. Machine Learn. Res. (2005) 2177-2204
    • (2005) J. Machine Learn. Res. , pp. 2177-2204
    • Opper, M.1    Winther, O.2
  • 49
    • 1542476948 scopus 로고    scopus 로고
    • M. Davies, N. Mitianoudis, A simple mixture model for sparse overcomplete ICA, IEE Proceedings on Vision, Image and Signal Processing, February 2004


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.