Generalization Error Bounds for Kernel Matrix Completion and Extrapolation

Pere Gimenez-Febrer, Alba Pages-Zamora, Georgios B. Giannakis

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


Prior information can be incorporated in matrix completion to improve estimation accuracy and extrapolate the missing entries. Reproducing kernel Hilbert spaces provide tools to leverage the said prior information, and derive more reliable algorithms. This paper analyzes the generalization error of such approaches, and presents numerical tests confirming the theoretical results.

Original languageEnglish (US)
Article number8974415
Pages (from-to)326-330
Number of pages5
JournalIEEE Signal Processing Letters
StatePublished - 2020
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 1994-2012 IEEE.


  • Matrix completion
  • Rademacher complexity
  • generalization error
  • kernel regression


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