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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

In this paper, we aim at the problem of tensor data completion. Tensor-traindecomposition is adopted because of its powerful representation ability andlinear scalability to tensor order. We propose an algorithm named SparseTensor-train Optimization (STTO) which considers incomplete data as sparsetensor and uses first-order optimization method to find the factors oftensor-train decomposition. Our algorithm is shown to perform well insimulation experiments at both low-order cases and high-order cases. We alsoemploy a tensorization method to transform data to a higher-order form toenhance the performance of our algorithm. The results of image recoveryexperiments in various cases manifest that our method outperforms othercompletion algorithms. Especially when the missing rate is very high, e.g.,90\% to 99\%, our method is significantly better than the state-of-the-artmethods.

High-order Tensor Completion for Data Recovery via Sparse Tensor-train Optimization