Missing data imputation is a critical process in data analysis, enabling researchers to infer plausible values for absent observations. Over recent decades, a variety of methods have emerged ...

However, most existing autoencoder-based methods discard the reconstruction of auxiliary information, which poses a huge challenge for better representation learning and model scalability.