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

Sparse linear prediction methods suffer from decreased prediction accuracywhen the predictor variables have cluster structure (e.g. there are highlycorrelated groups of variables). To improve prediction accuracy, variousmethods have been proposed to identify variable clusters from the data andintegrate cluster information into a sparse modeling process. But none of thesemethods achieve satisfactory performance for prediction, variable selection andvariable clustering simultaneously. This paper presents Variable ClusterPrincipal Component Regression (VC-PCR), a prediction method that supervisesvariable selection and variable clustering in order to solve this problem.Experiments with real and simulated data demonstrate that, compared tocompetitor methods, VC-PCR achieves better prediction, variable selection andclustering performance when cluster structure is present.

VC-PCR: A Prediction Method based on Supervised Variable Selection and Clustering