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

This paper generalizes the proportionate-type adaptive algorithm to the graphsignal processing and proposes two proportionate-type adaptive graph signalrecovery algorithms. The gain matrix of the proportionate algorithm leads tofaster convergence than least mean squares (LMS) algorithm. In this paper, thegain matrix is obtained in a closed-form by minimizing the gradient of themean-square deviation (GMSD). The first algorithm is the Proportionate-typeGraph LMS (Pt-GLMS) algorithm which simply uses a gain matrix in the recursionprocess of the LMS algorithm and accelerates the convergence of the Pt-GLMSalgorithm compared to the LMS algorithm. The second algorithm is theProportionate-type Graph Extended LMS (Pt-GELMS) algorithm, which uses theprevious signal vectors alongside the signal of the current iteration. ThePt-GELMS algorithm utilizes two gain matrices to control the effect of thesignal of the previous iterations. The stability analyses of the algorithms arealso provided. Simulation results demonstrate the efficacy of the two proposedproportionate-type LMS algorithms.

Proportionate Adaptive Graph Signal Recovery