  This work proposes a non-iterative strategy for missing value imputations
which is guided by similarity between observations, but instead of explicitly
determining distances or nearest neighbors, it assigns observations to
overlapping buckets through recursive semi-random hyperplane cuts, in which
weighted averages are determined as imputations for each variable. The quality
of these imputations is oftentimes not as good as that of chained equations,
but the proposed technique is much faster, non-iterative, can make imputations
on new data without re-calculating anything, and scales easily to large and
high-dimensional datasets, providing a significant boost over simple
mean/median imputation in regression and classification metrics with imputed
values when other methods are not feasible.
