Imputation of missing values in multi-view data

• Published in: Information fusion 2024-11, Vol.111, p.102524, Article 102524
• Main Authors: van Loon, Wouter, Fokkema, Marjolein, de Vos, Frank, Koini, Marisa, Schmidt, Reinhold, de Rooij, Mark
• Format: Article
• Language: English
• Subjects: Feature selection; Imputation; Missing data; Multi-view learning; Stacked generalization
• ISSN: 1566-2535; 1872-6305
• DOI: 10.1016/j.inffus.2024.102524

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. •A new imputation method for multi-view data is introduced.•The new method shows competitive results at a much lower computational cost.•The new method allows state-of-the-art algorithms to be used in much larger data sets than before.