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 a new iterative greedy algorithm for reconstructions of sparsesignals with or without noisy perturbations in compressed sensing. The proposedalgorithm, called \emph{subspace thresholding pursuit} (STP) in this paper, isa simple combination of subspace pursuit and iterative hard thresholding.Firstly, STP has the theoretical guarantee comparable to that of $\ell_1$minimization in terms of restricted isometry property. Secondly, with a tunedparameter, on the one hand, when reconstructing Gaussian signals, it canoutperform other state-of-the-art reconstruction algorithms greatly; on theother hand, when reconstructing constant amplitude signals with random signs,it can outperform other state-of-the-art iterative greedy algorithms and evenoutperform $\ell_1$ minimization if the undersampling ratio is not very large.In addition, we propose a simple but effective method to improve the empiricalperformance further if the undersampling ratio is large. Finally, it is showedthat other iterative greedy algorithms can improve their empirical performanceby borrowing the idea of STP.

Subspace Thresholding Pursuit: A Reconstruction Algorithm for Compressed Sensing