Subspace learning with partial information

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

Research output: Contribution to journalArticlepeer-review


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 languageEnglish
JournalJournal of Machine Learning Research
StatePublished - 1 Apr 2016
Externally publishedYes

Bibliographical note

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


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

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Statistics and Probability
  • Artificial Intelligence


Dive into the research topics of 'Subspace learning with partial information'. Together they form a unique fingerprint.

Cite this