  The goal of subspace learning is to find a $k$-dimensional subspace of
$\mathbb{R}^d$, such that the expected squared distance between instance
vectors and the subspace is as small as possible. In this paper we study
subspace learning in a partial information setting, in which the learner can
only observe $r \le d$ attributes from each instance vector. We propose several
efficient algorithms for this task, and analyze their sample complexity
