Subspace Learning with Partial Information

Alon Gonen; Dan Rosenbaum; Yonina C. Eldar; Shai Shalev-Shwartz

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.

Subspace Learning with Partial Information

Abstract