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 novel 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 the entire missing regions to generate and impute data as a whole. Considering the reasonable of the GIN results, IGM is assigned to capture coherent information between global and local and guide the LIN to look only at a small area centered at the missing focused regions. After the processing of these three modules, local imputed results are concatenated to global imputed results, which impute reasonable values and refine 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.