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

Recovering a large matrix from limited measurements is a challenging taskarising in many real applications, such as image inpainting, compressivesensing and medical imaging, and this kind of problems are mostly formulated aslow-rank matrix approximation problems. Due to the rank operator beingnon-convex and discontinuous, most of the recent theoretical studies use thenuclear norm as a convex relaxation and the low-rank matrix recovery problem issolved through minimization of the nuclear norm regularized problem. However, amajor limitation of nuclear norm minimization is that all the singular valuesare simultaneously minimized and the rank may not be well approximated\cite{hu2012fast}. Correspondingly, in this paper, we propose a new multi-stagealgorithm, which makes use of the concept of Truncated Nuclear NormRegularization (TNNR) proposed in \citep{hu2012fast} and Iterative SupportDetection (ISD) proposed in \citep{wang2010sparse} to overcome the abovelimitation. Besides matrix completion problems considered in\citep{hu2012fast}, the proposed method can be also extended to the generallow-rank matrix recovery problems. Extensive experiments well validate thesuperiority of our new algorithms over other state-of-the-art methods.

Truncated Nuclear Norm Minimization for Image Restoration Based On Iterative Support Detection