Subspace Learning with Partial Information

Journal of Machine Learning Research, Volume 17, Issue 1, 2016, Pages 52:1-52:21.

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Abstract:

The goal of subspace learning is to find a k-dimensional subspace of Rd, 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 ≤ d attributes from each instance vector. We ...More

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