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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...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Public Library of Science
2017
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| 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 |