Imputation of missing values in multi-view data
arxiv(2022)
摘要
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 leads to very large quantities of missing data which,
especially when combined with high-dimensionality, makes the application of
conditional imputation methods computationally infeasible. 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. 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.
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关键词
values,data,multi-view
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