  Inductive Matrix Completion (IMC) is an important class of matrix completion
problems that allows direct inclusion of available features to enhance
estimation capabilities. These models have found applications in personalized
recommendation systems, multilabel learning, dictionary learning, etc. This
paper examines a general class of noisy matrix completion tasks where the
underlying matrix is following an IMC model i.e., it is formed by a mixing
matrix (a priori unknown) sandwiched between two known feature matrices. The
mixing matrix here is assumed to be well approximated by the product of two
sparse matrices---referred here to as "sparse factor models." We leverage the
main theorem of Soni:2016:NMC and extend it to provide theoretical error bounds
for the sparsity-regularized maximum likelihood estimators for the class of
problems discussed in this paper. The main result is general in the sense that
it can be used to derive error bounds for various noise models. In this paper,
we instantiate our main result for the case of Gaussian noise and provide
corresponding error bounds in terms of squared loss.
