Given only a few observed entries from a low-rank matrix $X$, matrix
completion is the problem of imputing the missing entries, and it formalizes a
wide range of real-world settings that involve estimating missing data.
However, when there are too few observed entries to complete the matrix, what
other aspects of the underlying matrix can be reliably recover