Data imputation is the most popular method of dealing with missing values,
but in most real life applications, large missing data can occur and it is
difficult or impossible to evaluate whether data has been imputed accurately
(lack of ground truth). This paper addresses these issues by proposing an
effective and simple principal component based method for determining whether
individual data features can be accurately imputed - feature imputability. In
particular, we establish a strong linear relationship between principal
component loadings and feature imputability, even in the presence of extreme
missingness and lack of ground truth. This work will have important
implications in practical data imputation strategies.Comment: 5 pages, 3 figures, 1 table. In: Proceedings of the 37th
International Conference on Machine Learning (ICML), 202
