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We develop an approximation formula for the cross-validation error (CVE) of a sparse linear regression penalized by $\ell_1$-norm and total variation terms, which is based on a perturbative expansion utilizing the largeness of both the data dimensionality and the model.
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Honma M, Akiyama K, Tazaki F, Kuramochi K, Ikeda S, Hada K et al. Imaging black holes with sparse modeling. JPCS
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Ikeda S, Tazaki F, Akiyama K, Hada K. PRECL: A new method for interferometry imaging from closure phase. Publ. Astron. Soc. Japan
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Bouman K L, Johnson M D, Zoran D, Fish V L, Doeleman S S, Freeman W T. Computational Imaging for VLBI Image Reconstruction. The IEEE Conference on Computer Vision and Pattern Recognition, 913 (2016)
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http://sparse-modeling.jp/index _ \_ e.html
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2017
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Akiyama K, Kuramochi K, Ikeda S, Fish V, Tazaki F, Honma M et al. Imaging the Schwarzschild-radius-scale Structure of M87 with the Event Horizon Telescope Using Sparse Modeling. The Astrophysical Journal, 838
2017
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