Subspace Learning with Partial Information
Journal of Machine Learning Research, Volume 17, Issue 1, 2016, Pages 52:1-52:21.
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
PPT (Upload PPT)