Matrix approximation under local low-rank assumption

Joonseok Lee, Seungyeon Kim, Guy Lebanon, Yoram Singer

Research output: Contribution to conferencePaperpeer-review


Matrix approximation is a common tool in machine learning for building accurate prediction models for recommendation systems, text mining, and computer vision. A prevalent assumption in constructing matrix approximations is that the partially observed matrix is of low-rank. We propose a new matrix approximation model where we assume instead that the matrix is only locally of low-rank, leading to a representation of the observed matrix as a weighted sum of low-rank matrices. We analyze the accuracy of the proposed local low-rank modeling. Our experiments show improvements of prediction accuracy in recommendation tasks.

Original languageEnglish (US)
StatePublished - Jan 1 2013
Externally publishedYes
Event1st International Conference on Learning Representations, ICLR 2013 - Scottsdale, United States
Duration: May 2 2013May 4 2013


Conference1st International Conference on Learning Representations, ICLR 2013
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Education
  • Computer Science Applications
  • Linguistics and Language
  • Language and Linguistics


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