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 convex formulation of the fused lasso signal approximationproblem consisting of non-convex penalty functions. The fused lasso signalmodel aims to estimate a sparse piecewise constant signal from a noisyobservation. Originally, the $\ell_1$ norm was used as a sparsity-inducingconvex penalty function for the fused lasso signal approximation problem.However, the $\ell_1$ norm underestimates signal values. Non-convexsparsity-inducing penalty functions better estimate signal values. In thispaper, we show how to ensure the convexity of the fused lasso signalapproximation problem with non-convex penalty functions. We further derive acomputationally efficient algorithm using the majorization-minimizationtechnique. We apply the proposed fused lasso method for the detection ofpulses.

Convex Fused Lasso Denoising with Non-Convex Regularization and its use for Pulse Detection