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Subspace Learning with Partial Information

Alon Gonen, Dan Rosenbaum, Yonina C. Eldar, Shai Shalev-Shwartz; 17(52):1−21, 2016.

Abstract

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.

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