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

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 ℓ1-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 to...

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Bibliographic Details
Main Authors: Tomoyuki Obuchi, Shiro Ikeda, Kazunori Akiyama, Yoshiyuki Kabashima
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2017-01-01
Series:PLoS ONE
Online Access:http://europepmc.org/articles/PMC5720762?pdf=render

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