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, ranging ...

In the last decade, auxiliary information has been widely used to address data sparsity. Due to the advantages of feature extraction and the no-label requirement, autoencoder-based methods addressing ...