Predicting Feature Imputability in the Absence of Ground Truth

Niamh McCombe, Xuemei Ding, Girijesh Prasad, David P Finn, Stephen Todd, Paula L McClean, Kongfatt Wong-Lin

TL;DR: Using PCA methods to determine whether individual dataset features can be accurately imputed

Keywords: Missing Data, Data Imputation, Feature Imputability, Machine Learning, PCA, NIPALS

Abstract: 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.