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 Plus annual

Presents the access of premium content as premium feature

Premium Content

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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We propose and analyze an efficient algorithm for solving the joint sparserecovery problem using a new regularization-based method, named orthogonallyweighted $\ell_{2,1}$ ($\mathit{ow}\ell_{2,1}$), which is specifically designedto take into account the rank of the solution matrix. This method hasapplications in feature extraction, matrix column selection, and dictionarylearning, and it is distinct from commonly used $\ell_{2,1}$ regularization andother existing regularization-based approaches because it can exploit the fullrank of the row-sparse solution matrix, a key feature in many applications. Weprovide a proof of the method's rank-awareness, establish the existence ofsolutions to the proposed optimization problem, and develop an efficientalgorithm for solving it, whose convergence is analyzed. We also presentnumerical experiments to illustrate the theory and demonstrate theeffectiveness of our method on real-life problems.

Orthogonally weighted $\ell_{2,1}$ regularization for rank-aware joint sparse recovery: algorithm and analysis