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arXiv · 1906.08770

Generalization error bounds for kernel matrix completion and extrapolation

Pere Giménez-Febrer·Alba Pagès-Zamora·Georgios B. Giannakis
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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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Pere Giménez-Febrer, Alba Pagès-Zamora, Georgios B. Giannakis. 2019-06-20. Generalization error bounds for kernel matrix completion and extrapolation. https://doi.org/10.1109/lsp.2020.2970306

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