The Mixture of Gaussian Processes (MGP) is a powerful statistical learning framework in machine learning. For the learning of MGP on a given dataset, it is necessary to solve the model selection problem, i.e., to determine the number C of actual GP components in the mixture. However, the current learning algorithms for MGPs cannot solve this problem effectively. In this paper, we propose an effective model selection criterion, called the Synchronously Balancing or SB criterion for MGPs. It is demonstrated by the experimental results that this SB criterion is feasible and even outperforms two classical criterions: AIC and BIC, for model selection on MGPs. Moreover, it is found that there exists a feasible interval of the penalty coefficient for correct model selection.
CITATION STYLE
Zhao, L., Chen, Z., & Ma, J. (2015). An effective model selection criterion for mixtures of gaussian processes. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9377 LNCS, pp. 345–354). Springer Verlag. https://doi.org/10.1007/978-3-319-25393-0_38
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