  Datasets with missing values are very common in real world applications.
GAIN, a recently proposed deep generative model for missing data imputation,
has been proved to outperform many state-of-the-art methods. But GAIN only uses
a reconstruction loss in the generator to minimize the imputation error of the
non-missing part, ignoring the potential category information which can reflect
the relationship between samples. In this paper, we propose a novel
unsupervised missing data imputation method named PC-GAIN, which utilizes
potential category information to further enhance the imputation power.
Specifically, we first propose a pre-training procedure to learn potential
category information contained in a subset of low-missing-rate data. Then an
auxiliary classifier is determined using the synthetic pseudo-labels. Further,
this classifier is incorporated into the generative adversarial framework to
help the generator to yield higher quality imputation results. The proposed
method can improve the imputation quality of GAIN significantly. Experimental
results on various benchmark datasets show that our method is also superior to
other baseline approaches. Our code is available at
\url{https://github.com/WYu-Feng/pc-gain}.
