Gaussian Processes for Missing Value Imputation. (arXiv:2204.04648v2 [stat.ML] UPDATED)

Web: http://arxiv.org/abs/2204.04648

Missing values are common in many real-life datasets. However, most of the
current machine learning methods can not handle missing values. This means that
they should be imputed beforehand. Gaussian Processes (GPs) are non-parametric
models with accurate uncertainty estimates that combined with sparse
approximations and stochastic variational inference scale to large data sets.
Sparse GPs can be used to compute a predictive distribution for missing data.
Here, we present a hierarchical composition of sparse GPs that is used to
predict …

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