  Data imputation is an effective way to handle missing data, which is common
in practical applications. In this study, we propose and test a novel data
imputation process that achieve two important goals: (1) preserve the row-wise
similarities among observations and column-wise contextual relationships among
features in the feature matrix, and (2) tailor the imputation process to
specific downstream label prediction task. The proposed imputation process uses
Transformer network and graph structure learning to iteratively refine the
contextual relationships among features and similarities among observations.
Moreover, it uses a meta-learning framework to select features that are
influential to the downstream prediction task of interest. We conduct
experiments on real-world large data sets, and show that the proposed
imputation process consistently improves imputation and label prediction
performance over a variety of benchmark methods.
