  We study the problem of subspace tracking in the presence of missing data
(ST-miss). In recent work, we studied a related problem called robust ST. In
this work, we show that a simple modification of our robust ST solution also
provably solves ST-miss and robust ST-miss. To our knowledge, our result is the
first `complete' guarantee for ST-miss. This means that we can prove that under
assumptions on only the algorithm inputs, the output subspace estimates are
close to the true data subspaces at all times. Our guarantees hold under mild
and easily interpretable assumptions, and allow the underlying subspace to
change with time in a piecewise constant fashion. In contrast, all existing
guarantees for ST are partial results and assume a fixed unknown subspace.
Extensive numerical experiments are shown to back up our theoretical claims.
Finally, our solution can be interpreted as a provably correct mini-batch and
memory-efficient solution to low-rank Matrix Completion (MC).
