Subspace learning with partial information

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

Research output: Contribution to journalArticlepeer-review

29 Scopus citations

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 propose several efficient algorithms for this task, and analyze their sample complexity.

Original languageAmerican English
Article number52
Number of pages21
JournalJournal of Machine Learning Research
Volume17
StatePublished - 1 Apr 2016

Bibliographical note

Funding Information:
We thank the anonymous reviewers for their helpful comments. We also thank Amit Daniely for helpful discussions. This research has been supported by ISF no 1673/14.

Publisher Copyright:
©2016 Alon Gonen, Dan Rosenbaum, Yonina C. Eldar and Shai Shalev-Shwartz.

Keywords

  • Budgeted learning
  • Learning theory
  • Learning with partial information
  • Principal components analysis
  • Statistical learning

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