Abstract
The mixture of Gaussian process functional regressions (mix-GPFR) is a powerful tool for curve clustering and prediction. Unfortunately, there generally exist a large number of local maximums for the Q-function of the conventional EM algorithm so that the conventional EM algorithm is often trapped in the local maximum. In order to overcome this problem, we propose a deterministic annealing EM (DAEM) algorithm for mix-GPFR in this paper. The experimental results on the simulated and electrical load datasets demonstrate that the DAEM algorithm outperforms the conventional EM algorithm on parameter estimation, curve clustering and prediction.
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References
Rasmussen, C.E.: Evaluation of Gaussian Processes and Other Methods for Non-linear Regression. Ph.D. dissertation, Department of Computer Science, University of Toronto (1996)
Rasmussen, C.E., Williams, C.K.I.: Regression Gaussian Process for Machine Learning, ch. 2. MIT Press, Cambridge (2006)
Shi, J.Q., et al.: Gaussian process functional regression modeling for batch data. Biometrics 63, 714–723 (2007)
Boor, D.E.: On calculating with B-splines. J. Approximation Theory 6, 50–62 (1972)
Shi, J.Q., Murray-Smith, R., Titterington, D.M.: Bayesian regression and classification using mixtures of Gaussian process. Int. J. Adapt. Control Signal Process. 17, 149–161 (2003)
Shi, J.Q., Murray-Smith, R., Titterington, D.M.: Hierarchical Gaussian process mixtures for regression. Stat. Comput. 15, 31–41 (2005)
Kamnik, R., et al.: Nonlinear modeling of FES-supported standing-up in paraplegia for selection of feedback sensors. IEEE Trans. Neural Syst. Rehabil. Eng. 13(1), 40–52 (2005)
Qiang, Zhe, Ma, Jinwen: Automatic model selection of the mixtures of Gaussian processes for regression. In: Hu, X., Xia, Y., Zhang, Y., Zhao, D. (eds.) ISNN 2015. LNCS, vol. 9377, pp. 335–344. Springer, Heidelberg (2015). doi:10.1007/978-3-319-25393-0_37
Shi, J.Q., Wang, B.: Curve prediction and clustering with mixtures of Gaussian process functional regression models. Stat. Comput. 18, 267–283 (2008)
Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. J. R. Statist. Soc. (B) 39, 1–38 (1977)
Ueda, N., Nakano, R.: Mixture density estimation via EM algorithm with deterministic annealing. In: Proceedings of the IEEE Neural Networks for Signal Processing, pp. 66–77 (1994)
Ueda, N., Nakano, R.: Deterministic annealing EM algorithm. Neural Netw. 11, 271–282 (1998)
Ma, J., Xu, L., Jordan, M.I.: Asymptotic convergence rate of the EM algorithm for Gaussian mixtures. Neural Comput. 12(12), 2881–2907 (2000)
Mohandes, M.: Support vector machines for short-term electrical load forecasting. Int. J. Energy Res. 26, 335–345 (2002)
Acknowledgement
This work is supported by the National Science Foundation of China under Grant 61171138.
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Wu, D., Ma, J. (2016). A DAEM Algorithm for Mixtures of Gaussian Process Functional Regressions. In: Huang, DS., Han, K., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2016. Lecture Notes in Computer Science(), vol 9773. Springer, Cham. https://doi.org/10.1007/978-3-319-42297-8_28
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DOI: https://doi.org/10.1007/978-3-319-42297-8_28
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