[1] |
Williams C, Rasmussen C. Gaussian processes for regression. Advances in Neural Information Processing Systems, 1995, 8: 514-520.
|
[2] |
Rasmussen C E. Gaussian processes in machine learning. In: Summer School on Machine Learning. Berlin: Springer, 2004: 63-71.
|
[3] |
Shi J Q, Choi T.Gaussian Process Regression Analysis for Functional data. Boca Raton, FL: CRC Press, 2011.
|
[4] |
Sun B, Yao H, Liu T. Short-term wind speed forecasting based on Gaussian process regression model. Proceedings of the Chinese Society for Electrical Engineering, 2012, 32(29): 104-109.
|
[5] |
Liu K Y, Fang Y, Liu B G, et al. Intelligent deformation prediction model of tunnel surrounding rock based on genetic-Gaussian process regression coupling algorithm. Journal of the China Railway Society, 2011, 33: 101-106. (In Chinese)
|
[6] |
Smola A J, Bartlett P L. Sparse greedy Gaussian process regression. In: Advances in Neural Information Processing Systems 13. Cambridge, MA: MIT Press, 2001: 619-625.
|
[7] |
Seiferth D, Chowdhary G, Mühlegg M, et al. Online Gaussian process regression with non-Gaussian likelihood. In 2017 American Control Conference (ACC). IEEE, 2017: 3134-3140.
|
[8] |
Banerjee A, Dunson D B, Tokdar S T. Efficient Gaussian process regression for large datasets. Biometrika, 2013, 100: 75-89.
|
[9] |
Wauthier F L, Jordan M I. Heavy-tailed process priors for selective shrinkage. In: Advances in Neural Information Processing Systems 23. Cambridge, MA: MIT Press, 2010: 2406-2414.
|
[10] |
Yu S, Tresp V, Yu K, et al. Robust multi-task learning with t-processes. In: Proceedings of the 24th International Conference on Machine learning. New York: Association for Computing Machinery, 2007: 1103-1110.
|
[11] |
Shah A, Wilson A, Ghahramani Z. Student-t processes as alternatives to Gaussian processes. In: Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics. Cambridge, MA: PMLR, 2014: 877-885.
|
[12] |
Jylänki P, Vanhatalo J, Vehtari A. Robust Gaussian process regression with a student-t likelihood. Journal of Machine Learning Research, 2011, 12: 3227-3257.
|
[13] |
Wang Z, Shi J Q, Lee Y. Extended t-process regression models. Journal of Statistical Planning and Inference, 2017, 189: 38-60.
|
[14] |
Lin Z, Yao F. Functional regression on manifold with contamination.https://arxiv.org/abs/1704.03005.
|
[15] |
Sober B, Aizenbud Y, Levin D.Approximation of functions over manifolds: A moving least-squares approach.https://arxiv.org/abs/1711.00765.
|
[16] |
Zhou Zhihua, Zhan Dechuan. A manifold learning-based multi-instance regression algorithm. Chinese Journal of Computers, 2006, 29(11): 1948-1955. (In Chinese)
|
[17] |
Gao Y, Liu Y J. Diversity based discriminant muti-manifold learning for dimensionality reduction. Automation and Instrumentation, 2020(4): 30-34. (In Chinese)
|
[18] |
Fan J F, Chen D C. Combining manifold learning and nonlinear regression for head pose estimation. Journal of Image and Graphics, 2012, 17(8): 1002-1010. (In Chinese)
|
[19] |
Calandra R, Peters J, Rasmussen C E, et al. Manifold Gaussian processes for regression. In: 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, 2016: 3338-3345.
|
[20] |
Mallasto A, Feragen A. Wrapped Gaussian process regression on Riemannian manifolds. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2018: 5580-5588.
|
[21] |
Lee Y, Nelder J A. Double hierarchical generalized linear models (with discussion). Journal of the Royal Statistical Society: Series C (Applied Statistics), 2006, 55: 139-185.
|
[22] |
Xu P, Lee Y, Shi J Q, et al. Automatic detection of significant areas for functional data with directional error control. Statistics in Medicine, 2019, 38: 376-397.
|
[23] |
Seeger M W, Kakade S M, Foster D P. Information consistency of nonparametric Gaussian process methods. IEEE Transactions on Information Theory, 2008, 54: 2376-2382.
|
[24] |
Berlinet A, Thomas-Agnan C. Reproducing Kernel Hilbert Spaces in Probability and Statistics. Berlin: Springer Science & Business Media, 2011.
|
[25] |
Hampel F R, Ronchetti E M, Rousseeuw P J, et al. Robust Statistics: The Approach Based on Influence Functions. Hobooken, NJ: Wiley, 2011.
|
[1] |
Williams C, Rasmussen C. Gaussian processes for regression. Advances in Neural Information Processing Systems, 1995, 8: 514-520.
|
[2] |
Rasmussen C E. Gaussian processes in machine learning. In: Summer School on Machine Learning. Berlin: Springer, 2004: 63-71.
|
[3] |
Shi J Q, Choi T.Gaussian Process Regression Analysis for Functional data. Boca Raton, FL: CRC Press, 2011.
|
[4] |
Sun B, Yao H, Liu T. Short-term wind speed forecasting based on Gaussian process regression model. Proceedings of the Chinese Society for Electrical Engineering, 2012, 32(29): 104-109.
|
[5] |
Liu K Y, Fang Y, Liu B G, et al. Intelligent deformation prediction model of tunnel surrounding rock based on genetic-Gaussian process regression coupling algorithm. Journal of the China Railway Society, 2011, 33: 101-106. (In Chinese)
|
[6] |
Smola A J, Bartlett P L. Sparse greedy Gaussian process regression. In: Advances in Neural Information Processing Systems 13. Cambridge, MA: MIT Press, 2001: 619-625.
|
[7] |
Seiferth D, Chowdhary G, Mühlegg M, et al. Online Gaussian process regression with non-Gaussian likelihood. In 2017 American Control Conference (ACC). IEEE, 2017: 3134-3140.
|
[8] |
Banerjee A, Dunson D B, Tokdar S T. Efficient Gaussian process regression for large datasets. Biometrika, 2013, 100: 75-89.
|
[9] |
Wauthier F L, Jordan M I. Heavy-tailed process priors for selective shrinkage. In: Advances in Neural Information Processing Systems 23. Cambridge, MA: MIT Press, 2010: 2406-2414.
|
[10] |
Yu S, Tresp V, Yu K, et al. Robust multi-task learning with t-processes. In: Proceedings of the 24th International Conference on Machine learning. New York: Association for Computing Machinery, 2007: 1103-1110.
|
[11] |
Shah A, Wilson A, Ghahramani Z. Student-t processes as alternatives to Gaussian processes. In: Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics. Cambridge, MA: PMLR, 2014: 877-885.
|
[12] |
Jylänki P, Vanhatalo J, Vehtari A. Robust Gaussian process regression with a student-t likelihood. Journal of Machine Learning Research, 2011, 12: 3227-3257.
|
[13] |
Wang Z, Shi J Q, Lee Y. Extended t-process regression models. Journal of Statistical Planning and Inference, 2017, 189: 38-60.
|
[14] |
Lin Z, Yao F. Functional regression on manifold with contamination.https://arxiv.org/abs/1704.03005.
|
[15] |
Sober B, Aizenbud Y, Levin D.Approximation of functions over manifolds: A moving least-squares approach.https://arxiv.org/abs/1711.00765.
|
[16] |
Zhou Zhihua, Zhan Dechuan. A manifold learning-based multi-instance regression algorithm. Chinese Journal of Computers, 2006, 29(11): 1948-1955. (In Chinese)
|
[17] |
Gao Y, Liu Y J. Diversity based discriminant muti-manifold learning for dimensionality reduction. Automation and Instrumentation, 2020(4): 30-34. (In Chinese)
|
[18] |
Fan J F, Chen D C. Combining manifold learning and nonlinear regression for head pose estimation. Journal of Image and Graphics, 2012, 17(8): 1002-1010. (In Chinese)
|
[19] |
Calandra R, Peters J, Rasmussen C E, et al. Manifold Gaussian processes for regression. In: 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, 2016: 3338-3345.
|
[20] |
Mallasto A, Feragen A. Wrapped Gaussian process regression on Riemannian manifolds. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2018: 5580-5588.
|
[21] |
Lee Y, Nelder J A. Double hierarchical generalized linear models (with discussion). Journal of the Royal Statistical Society: Series C (Applied Statistics), 2006, 55: 139-185.
|
[22] |
Xu P, Lee Y, Shi J Q, et al. Automatic detection of significant areas for functional data with directional error control. Statistics in Medicine, 2019, 38: 376-397.
|
[23] |
Seeger M W, Kakade S M, Foster D P. Information consistency of nonparametric Gaussian process methods. IEEE Transactions on Information Theory, 2008, 54: 2376-2382.
|
[24] |
Berlinet A, Thomas-Agnan C. Reproducing Kernel Hilbert Spaces in Probability and Statistics. Berlin: Springer Science & Business Media, 2011.
|
[25] |
Hampel F R, Ronchetti E M, Rousseeuw P J, et al. Robust Statistics: The Approach Based on Influence Functions. Hobooken, NJ: Wiley, 2011.
|