Machine learning data repair systems (e.g. HoloClean) have achieved state-of-the-art performance for the data repair problem on many datasets. However, these systems face significant challenges with sparse datasets. In this work, the challenges presented by such datasets to machine learning data repair systems are investigated. Dataset-independent methods are presented to mitigate the effects of data sparseness. Finally, experimental results are validated on a large, sparse real-world dataset: Census. Showing that the problem size can be reduced by more than 70%, saving significant computational costs, while still getting high accuracy data repairs (94.5% accuracy)
