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 propose a novel $\ell_1+\ell_2$-penalty, which we refer to as theGeneralized Elastic Net, for regression problems where the feature vectors areindexed by vertices of a given graph and the true signal is believed to besmooth or piecewise constant with respect to this graph. Under the assumptionof correlated Gaussian design, we derive upper bounds for the prediction andestimation errors, which are graph-dependent and consist of a parametric ratefor the unpenalized portion of the regression vector and another term thatdepends on our network alignment assumption. We also provide a coordinatedescent procedure based on the Lagrange dual objective to compute thisestimator for large-scale problems. Finally, we compare our proposed estimatorto existing regularized estimators on a number of real and synthetic datasetsand discuss its potential limitations.

The Generalized Elastic Net for least squares regression with network-aligned signal and correlated design