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/

Zendy Plus

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

Zendy Tools

Zendy Open

Motivated by a series of applications in data integration, languagetranslation, bioinformatics, and computer vision, we consider sphericalregression with two sets of unit-length vectors when the data are corrupted bya small fraction of mismatch in the response-predictor pairs. We propose athree-step algorithm in which we initialize the parameters by solving anorthogonal Procrustes problem to estimate a translation matrix $\mathbb{W}$ignoring the mismatch. We then estimate a mapping matrix aiming to correct themismatch using hard-thresholding to induce sparsity, while incorporatingpotential group information. We eventually obtain a refined estimate for$\mathbb{W}$ by removing the estimated mismatched pairs. We derive the errorbound for the initial estimate of $\mathbb{W}$ in both fixed andhigh-dimensional setting. We demonstrate that the refined estimate of$\mathbb{W}$ achieves an error rate that is as good as if no mismatch ispresent. We show that our mapping recovery method not only correctlydistinguishes one-to-one and one-to-many correspondences, but also consistentlyidentifies the matched pairs and estimates the weight vector for combinedcorrespondence. We examine the finite sample performance of the proposed methodvia extensive simulation studies, and with application to the unsupervisedtranslation of medical codes using electronic health records data.

Spherical Regression under Mismatch Corruption with Application to Automated Knowledge Translation