  In $masked\ low-rank\ approximation$, one is given $A \in \mathbb{R}^{n
\times n}$ and binary mask matrix $W \in \{0,1\}^{n \times n}$. The goal is to
find a rank-$k$ matrix $L$ for which: $$cost(L) = \sum_{i=1}^{n} \sum_{j =
1}^{n} W_{i,j} \cdot (A_{i,j} - L_{i,j} )^2 \leq OPT + \epsilon \|A\|_F^2 ,$$
where $OPT = \min_{rank-k\ \hat{L}} cost(\hat L)$ and $\epsilon$ is a given
error parameter. Depending on the choice of $W$, this problem captures factor
analysis, low-rank plus diagonal decomposition, robust PCA, low-rank matrix
completion, low-rank plus block matrix approximation, and many problems. Many
of these problems are NP-hard, and while some algorithms with provable
guarantees are known, they either 1) run in time $n^{\Omega(k^2/\epsilon)}$ or
2) make strong assumptions, e.g., that $A$ is incoherent or that $W$ is random.
  In this work, we show that a common polynomial time heuristic, which simply
sets $A$ to $0$ where $W$ is $0$, and then finds a standard low-rank
approximation, yields bicriteria approximation guarantees for this problem. In
particular, for rank $k' > k$ depending on the $public\ coin\ partition\
number$ of $W$, the heuristic outputs rank-$k'$ $L$ with cost$(L) \leq OPT +
\epsilon \|A\|_F^2$. This partition number is in turn bounded by the
$randomized\ communication\ complexity$ of $W$, when interpreted as a
two-player communication matrix. For many important examples of masked low-rank
approximation, including all those listed above, this result yields bicriteria
approximation guarantees with $k' = k \cdot poly(\log n/\epsilon)$.
  Further, we show that different models of communication yield algorithms for
natural variants of masked low-rank approximation. For example, multi-player
number-in-hand communication complexity connects to masked tensor decomposition
and non-deterministic communication complexity to masked Boolean low-rank
factorization.
