  Missing data is a commonly occurring problem in practice. Many imputation
methods have been developed to fill in the missing entries. However, not all of
them can scale to high-dimensional data, especially the multiple imputation
techniques. Meanwhile, the data nowadays tends toward high-dimensional.
Therefore, in this work, we propose Principal Component Analysis Imputation
(PCAI), a simple but versatile framework based on Principal Component Analysis
(PCA) to speed up the imputation process and alleviate memory issues of many
available imputation techniques, without sacrificing the imputation quality in
term of MSE. In addition, the frameworks can be used even when some or all of
the missing features are categorical, or when the number of missing features is
large. Next, we introduce PCA Imputation - Classification (PIC), an application
of PCAI for classification problems with some adjustments. We validate our
approach by experiments on various scenarios, which shows that PCAI and PIC can
work with various imputation algorithms, including the state-of-the-art ones
and improve the imputation speed significantly, while achieving competitive
mean square error/classification accuracy compared to direct imputation (i.e.,
impute directly on the missing data).
