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

The problem of recovering a low-rank matrix from the linear constraints,known as affine matrix rank minimization problem, has been attracting extensiveattention in recent years. In general, affine matrix rank minimization problemis a NP-hard. In our latest work, a non-convex fraction function is studied toapproximate the rank function in affine matrix rank minimization problem andtranslate the NP-hard affine matrix rank minimization problem into atransformed affine matrix rank minimization problem. A scheme of iterativesingular value thresholding algorithm is generated to solve the regularizedtransformed affine matrix rank minimization problem. However, one of thedrawbacks for our iterative singular value thresholding algorithm is that theparameter $a$, which influences the behaviour of non-convex fraction functionin the regularized transformed affine matrix rank minimization problem, needsto be determined manually in every simulation. In fact, how to determine theoptimal parameter $a$ is not an easy problem. Here instead, in this paper, wewill generate an adaptive iterative singular value thresholding algorithm tosolve the regularized transformed affine matrix rank minimization problem. Whendoing so, our new algorithm will be intelligent both for the choice of theregularized parameter $\lambda$ and the parameter $a$.

Adaptive iterative singular value thresholding algorithm to low-rank matrix recovery