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Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distribution
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en
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10
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Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, Proceedings of Machine Learning Research ; Volume 89
Abstract
Variable selection for Gaussian process models is often done using automatic relevance determination, which uses the inverse lengthscale parameter of each input variable as a proxy for variable relevance. This implicitly determined relevance has several drawbacks that prevent the selection of optimal input variables in terms of predictive performance. To improve on this, we propose two novel variable selection methods for Gaussian process models that utilize the predictions of a full model in the vicinity of the training points and thereby rank the variables based on their predictive relevance. Our empirical results on synthetic and real world data sets demonstrate improved variable selection compared to automatic relevance determination in terms of variability and predictive performance.
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Paananen, T, Piironen, J, Andersen, M & Vehtari, A 2019, Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distribution. in Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research, vol. 89, JMLR, International Conference on Artificial Intelligence and Statistics, Naha, Japan, 16/04/2019. < http://proceedings.mlr.press/v89/paananen19a.html >