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

Low-rank modeling generally refers to a class of methods that solve problemsby representing variables of interest as low-rank matrices. It has achievedgreat success in various fields including computer vision, data mining, signalprocessing and bioinformatics. Recently, much progress has been made intheories, algorithms and applications of low-rank modeling, such as exactlow-rank matrix recovery via convex programming and matrix completion appliedto collaborative filtering. These advances have brought more and moreattentions to this topic. In this paper, we review the recent advance oflow-rank modeling, the state-of-the-art algorithms, and related applications inimage analysis. We first give an overview to the concept of low-rank modelingand challenging problems in this area. Then, we summarize the models andalgorithms for low-rank matrix recovery and illustrate their advantages andlimitations with numerical experiments. Next, we introduce a few applicationsof low-rank modeling in the context of image analysis. Finally, we concludethis paper with some discussions.

Low-Rank Modeling and Its Applications in Image Analysis