Subspace Learning with Partial Information.
JOURNAL OF MACHINE LEARNING RESEARCH(2016)
摘要
The goal of subspace learning is to find a k-dimensional subspace of 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 <= d attributes from each instance vector. We propose several efficient algorithms for this task, and analyze their sample complexity.
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关键词
principal components analysis,budgeted learning,statistical learning,learning with partial information,learning theory
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