A multi-scale approach for data imputation

A common pre-possessing task in machine learning is to complete missing data entries in order to form a full dataset. In case the dimension of the input data is high, it is often the case that the rows and columns are correlated. In this work, we construct a multi-scale model that is based on the the dual row-column geometry of the dataset and apply it to imputation, which is carried out within the model construction. Experimental results demonstrate the efficiency of our approach on a publicly available dataset.