  We provide new approximation guarantees for greedy low rank matrix estimation
under standard assumptions of restricted strong convexity and smoothness. Our
novel analysis also uncovers previously unknown connections between the low
rank estimation and combinatorial optimization, so much so that our bounds are
reminiscent of corresponding approximation bounds in submodular maximization.
Additionally, we also provide statistical recovery guarantees. Finally, we
present empirical comparison of greedy estimation with established baselines on
two important real-world problems.
