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Accelerating cross-validation with total variation and its application to super-resolution imaging

We develop an approximation formula for the cross-validation error (CVE) of a sparse linear regression penalized by ℓ(1)-norm and total variation terms, which is based on a perturbative expansion utilizing the largeness of both the data dimensionality and the model. The developed formula allows us t...

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Detalles Bibliográficos
Autores principales: Obuchi, Tomoyuki, Ikeda, Shiro, Akiyama, Kazunori, Kabashima, Yoshiyuki
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5720762/
https://www.ncbi.nlm.nih.gov/pubmed/29216215
http://dx.doi.org/10.1371/journal.pone.0188012