I. Introduction
Big data means not only that the volume of acquired data is large, but the dimensionality of the dataset is also considerable. Matrix completion (MC) [1] is an example of this “curse of dimensionality” problem, where two large dimensional item sets (e.g., viewers and movies in the famed Netflix challenge) are correlated within and across sets. Specifically, given a small subset of pairwise observations (e.g., viewers’ ratings on movies), an MC algorithm reconstructs missing entries in the target matrix signal. Many MC algorithms have been devised using different priors to regularize the under-determined inverse problem, such as low rank of the target matrix [2] and graph signal smoothness priors [3]. See [4] for an introductory exposition.