Generalization Error Bounds for Kernel Matrix Completion and Extrapolation

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

doi:10.1109/LSP.2020.2970306

Generalization Error Bounds for Kernel Matrix Completion and Extrapolation

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

Research output: Contribution to journal › Article › peer-review

6 Scopus citations

Abstract

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 language	English (US)
Article number	8974415
Pages (from-to)	326-330
Number of pages	5
Journal	IEEE Signal Processing Letters
Volume	27
DOIs	https://doi.org/10.1109/LSP.2020.2970306
State	Published - 2020
Externally published	Yes

Bibliographical note

Publisher Copyright:
© 1994-2012 IEEE.

Keywords

Matrix completion
Rademacher complexity
generalization error
kernel regression

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title = "Generalization Error Bounds for Kernel Matrix Completion and Extrapolation",

abstract = "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.",

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Generalization Error Bounds for Kernel Matrix Completion and Extrapolation

Abstract

Bibliographical note

Keywords

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