Imputation of missing values in multi-view data

TL;DR

This paper presents a novel multi-view data imputation method that leverages the data's structure to reduce computational complexity, enabling efficient and competitive missing data imputation in high-dimensional multi-view datasets.

Contribution

It introduces a new imputation approach based on stacked penalized logistic regression that reduces dimensionality, improving efficiency over existing methods.

Findings

The new method achieves competitive imputation accuracy.

It significantly reduces computational time compared to traditional algorithms.

Enables use of advanced imputation techniques in high-dimensional multi-view data.

Abstract

Data for which a set of objects is described by multiple distinct feature sets (called views) is known as multi-view data. When missing values occur in multi-view data, all features in a view are likely to be missing simultaneously. This may lead to very large quantities of missing data which, especially when combined with high-dimensionality, can make the application of conditional imputation methods computationally infeasible. However, the multi-view structure could be leveraged to reduce the complexity and computational load of imputation. We introduce a new imputation method based on the existing stacked penalized logistic regression (StaPLR) algorithm for multi-view learning. It performs imputation in a dimension-reduced space to address computational challenges inherent to the multi-view context. We compare the performance of the new imputation method with several existing imputation algorithms in simulated data sets and a real data application. The results show that the new imputation method leads to competitive results at a much lower computational cost, and makes the use of advanced imputation algorithms such as missForest and predictive mean matching possible in settings where they would otherwise be computationally infeasible.