Imputation of missing values in multi-view data
| Autor: | van Loon, Wouter, Fokkema, Marjolein, de Vos, Frank, Koini, Marisa, Schmidt, Reinhold, de Rooij, Mark |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Information Fusion 111 (2024) 102524 |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1016/j.inffus.2024.102524 |
| Popis: | 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. Comment: 49 pages, 15 figures. Accepted manuscript |
| Databáze: | arXiv |
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