A Global to Local Guiding Network for Missing Data Imputation

Missing data imputation aims to accurately impute the unobserved regions with complete data in real world. Although many recent methods have made remarkable advances, the local homogenous regions especially in boundary and the reasonable of the imputed data are still two most challenging issues. To address these issues, we propose a Global to Local Guiding Network (G2LGN) based on generative adversarial network for missing data imputation, which is composed of a Global-Impute-Net (GIN), a Local-Impute-Net (LIN) and an Impute Guider Model (IGM). The GIN looks at entire missing regions to generate and impute data as a whole. Considering the reasonable of GIN results, IGM is assigned to capture coherent information between global and local and guide LIN to look only at small areas centered at missing focused regions. After the processing of these three modules, the local imputed results are concatenated to those global imputed results, which impute the reasonable values and refine the local details from rough to accurate. The comprehensive experiments on both numeric datasets and image dataset demonstrate our method is significantly superior to other 3 state-of-the-art approaches and 7 traditional methods. Besides, the extensive ablation study validates the superior performance for dealing with missing data imputation.