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

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  • Tomoyuki Obuchi
  • Shiro Ikeda
  • Kazunori Akiyama
  • Yoshiyuki Kabashima

Abstract

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 reduce the necessary computational cost of the CVE evaluation significantly. The practicality of the formula is tested through application to simulated black-hole image reconstruction on the event-horizon scale with super resolution. The results demonstrate that our approximation reproduces the CVE values obtained via literally conducted cross-validation with reasonably good precision.

Suggested Citation

  • Tomoyuki Obuchi & Shiro Ikeda & Kazunori Akiyama & Yoshiyuki Kabashima, 2017. "Accelerating cross-validation with total variation and its application to super-resolution imaging," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-14, December.
    • Handle: RePEc:plo:pone00:0188012
    DOI: 10.1371/journal.pone.0188012
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    Cited by:

    1. ZhengQiang Xiong & Qiuze Yu & Tao Sun & Wen Chen & Yuhao Wu & Jie Yin, 2020. "Super-resolution reconstruction of real infrared images acquired with unmanned aerial vehicle," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-18, June.

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