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Volumn 9, Issue , 2010, Pages 844-851

Bayesian Gaussian process latent variable model

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN GAUSSIAN PROCESS; DIMENSIONALITY REDUCTION; GAUSSIAN PROCESS MODELS; GAUSSIAN PROCESSES; INPUT VARIABLES; LATENT VARIABLE MODELS; LOWER BOUNDS; MARGINAL LIKELIHOOD; NONLINEAR DIMENSIONALITY REDUCTION; OVERFITTING; REAL-WORLD DATASETS; TRAINING PROCEDURES; VARIATIONAL INFERENCE;

EID: 84862302424     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper
Times cited : (367)

References (23)
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    • (1999) Advances in Neural Information Processing Systems , vol.11 , pp. 482-388
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    • C. M. Bishop and G. D. James. Analysis of multiphase flows using dual-energy gamma densitometry and neural networks. Nuclear Instruments and Methods in Physics Research, A327: 580-593, 1993.
    • (1993) Nuclear Instruments and Methods in Physics Research , vol.A327 , pp. 580-593
    • Bishop, C.M.1    James, G.D.2
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    • (2002) Neural Computation , vol.14 , Issue.3 , pp. 641-668
    • Csató, L.1    Opper, M.2
  • 8
    • 84898934543 scopus 로고    scopus 로고
    • Variational inference for Bayesian mixtures of factor analysers
    • S. A. Solla, T. K. Leen, and K.-R. Müller, editors, Cambridge, MA. MIT Press
    • Z. Ghahramani andM. J. Beal. Variational inference for Bayesian mixtures of factor analysers. In S. A. Solla, T. K. Leen, and K.-R. Müller, editors, Advances in Neural Information Processing Systems, volume 12, pages 831-864, Cambridge, MA, 2000. MIT Press.
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    • Ghahramani, Z.1    Beal, M.J.2
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    • Gaussian process models for visualisation of high dimensional data
    • S. Thrun, L. Saul, and B. Schölkopf, editors, Cambridge,MA. MIT Press
    • N. D. Lawrence. Gaussian process models for visualisation of high dimensional data. In S. Thrun, L. Saul, and B. Schölkopf, editors, Advances in Neural Information Processing Systems, volume 16, pages 329-336, Cambridge,MA, 2004.MIT Press.
    • (2004) Advances in Neural Information Processing Systems , vol.16 , pp. 329-336
    • Lawrence, N.D.1
  • 11
    • 84862289690 scopus 로고    scopus 로고
    • Learning for larger datasets with the Gaussian process latent variable model
    • M. Meila and X. Shen, editors, San Juan, Puerto Rico, 21-24 March. Omnipress
    • N. D. Lawrence. Learning for larger datasets with the Gaussian process latent variable model. In M. Meila and X. Shen, editors, Proceedings of the Eleventh International Workshop on Artificial Intelligence and Statistics, pages 243-250, San Juan, Puerto Rico, 21-24 March 2007. Omnipress.
    • (2007) Proceedings of the Eleventh International Workshop on Artificial Intelligence and Statistics , pp. 243-250
    • Lawrence, N.D.1
  • 12
    • 27844605876 scopus 로고    scopus 로고
    • Probabilistic non-linear principal component analysis with Gaussian process latent variable models
    • 11
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    • Automatic choice of dimensionality for PCA
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    • M. Titsias, N. D. Lawrence, and M. Rattray. Efficient sampling for Gaussian process inference using control variables. In D. Koller, Y. Bengio, D. Schuurmans, and L. Bottou, editors, Advances in Neural Information Processing Systems, volume 21, Cambridge, MA, 2009. MIT Press.
    • (2009) Advances in Neural Information Processing Systems , vol.21
    • Titsias, M.1    Lawrence, N.D.2    Rattray, M.3
  • 21
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    • Variational learning of inducing variables in sparse Gaussian processes
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    • M. K. Titsias. Variational learning of inducing variables in sparse Gaussian processes. In D. van Dyk and M. Welling, editors, Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, volume 5, pages 567-574, Clearwater Beach, FL, 16-18 April 2009. JMLR W&CP 5.
    • (2009) Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics , vol.5 , pp. 567-574
    • Titsias, M.K.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.