Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz
Gewählte Publikation:
SHR Neuro Krebs Kardio Lipid Stoffw Microb
van Loon, W; de Vos, F; de Vos, F; Koini, M; Schmidt, R; de Rooij, M.
Imputation of missing values in multi-view data
INFORM FUSION. 2024; 111: 102524
Doi: 10.1016/j.inffus.2024.102524
Web of Science
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- Co-Autor*innen der Med Uni Graz
- Koini Marisa
- Schmidt Reinhold
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- 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.
- Find related publications in this database (Keywords)
- Missing data
- Imputation
- Multi-view learning
- Stacked generalization
- Feature selection