Imputation of missing values in multi-view data

• Published in: arXiv.org 2024-06
• Main Authors: Wouter van Loon, Fokkema, Marjolein, de Vos, Frank, Koini, Marisa, Schmidt, Reinhold, de Rooij, Mark
• Format: Article
• Language: English
• Subjects: Algorithms; Missing data
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.2210.14484

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.