  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.
