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 study the tensor robust principal component analysis (TRPCA) problem, atensorial extension of matrix robust principal component analysis (RPCA), thataims to split the given tensor into an underlying low-rank component and asparse outlier component. This work proposes a fast algorithm, called RobustTensor CUR Decompositions (RTCUR), for large-scale non-convex TRPCA problemsunder the Tucker rank setting. RTCUR is developed within a framework ofalternating projections that projects between the set of low-rank tensors andthe set of sparse tensors. We utilize the recently developed tensor CURdecomposition to substantially reduce the computational complexity in eachprojection. In addition, we develop four variants of RTCUR for differentapplication settings. We demonstrate the effectiveness and computationaladvantages of RTCUR against state-of-the-art methods on both synthetic andreal-world datasets.

Robust Tensor CUR Decompositions: Rapid Low-Tucker-Rank Tensor Recovery with Sparse Corruption