Local low-rank matrix approximation

Joonseok Lee, Seungyeon Kim, Guy Lebanon, Yoram Singer

Research output: Contribution to conferencePaper

48 Scopus citations

Abstract

Matrix approximation is a common tool in 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 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 in prediction accuracy over classical approaches for recommendation tasks.

Original languageEnglish (US)
Pages741-749
Number of pages9
StatePublished - Jan 1 2013
Externally publishedYes
Event30th International Conference on Machine Learning, ICML 2013 - Atlanta, GA, United States
Duration: Jun 16 2013Jun 21 2013

Other

Other30th International Conference on Machine Learning, ICML 2013
CountryUnited States
CityAtlanta, GA
Period6/16/136/21/13

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Sociology and Political Science

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  • Cite this

    Lee, J., Kim, S., Lebanon, G., & Singer, Y. (2013). Local low-rank matrix approximation. 741-749. Paper presented at 30th International Conference on Machine Learning, ICML 2013, Atlanta, GA, United States.