  Prior information can be incorporated in matrix completion to improve
estimation accuracy and extrapolate the missing entries. Reproducing kernel
Hilbert spaces provide tools to leverage the said prior information, and derive
more reliable algorithms. This paper analyzes the generalization error of such
approaches, and presents numerical tests confirming the theoretical results.
