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 consider a problem of significant practical importance, namely, thereconstruction of a low-rank data matrix from a small subset of its entries.This problem appears in many areas such as collaborative filtering, computervision and wireless sensor networks. In this paper, we focus on the matrixcompletion problem in the case when the observed samples are corrupted bynoise. We compare the performance of three state-of-the-art matrix completionalgorithms (OptSpace, ADMiRA and FPCA) on a single simulation platform andpresent numerical results. We show that in practice these efficient algorithmscan be used to reconstruct real data matrices, as well as randomly generatedmatrices, accurately.

Low-rank Matrix Completion with Noisy Observations: a Quantitative Comparison