English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Sparse recovery via L1-penalized minimization can be typically solved by iteratively reweighted least squares (IRLS). The reconstruction quality is generally sensitive to the value of regularization parameter. In this wok, we propose a novel data-driven optimization scheme based on minimization of unbiased predictive risk estimate (UPRE). For a given IRLS iterate, we decompose and linearize the update by matrix splitting strategy, which makes the computation of Jacobian matrix tractable; and the UPRE of the IRLS iterate can be recursively evaluated. The developed recursive UPRE enables us to monitor the reconstruction error during the IRLS iterations, which can be applied to optimize regularization parameter. Numerical tests demonstrate the reliability of the recursive UPRE and the optimality of sparse reconstruction.

Optimization of Sparse Recovery Based on Recursive Unbiased Predictive Risk Estimate