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

Graph learning from signals is a core task in Graph Signal Processing (GSP).One of the most commonly used models to learn graphs from stationary signals isSpecT. However, its practical formulation rSpecT is known to be sensitive tohyperparameter selection and, even worse, to suffer from infeasibility. In thispaper, we give the first condition that guarantees the infeasibility of rSpecTand design a novel model (LogSpecT) and its practical formulation (rLogSpecT)to overcome this issue. Contrary to rSpecT, the novel practical model rLogSpecTis always feasible. Furthermore, we provide recovery guarantees of rLogSpecT,which are derived from modern optimization tools related to epi-convergence.These tools could be of independent interest and significant for variouslearning problems. To demonstrate the advantages of rLogSpecT in practice, ahighly efficient algorithm based on the linearized alternating direction methodof multipliers (L-ADMM) is proposed. The subproblems of L-ADMM admitclosed-form solutions and the convergence is guaranteed. Extensive numericalresults on both synthetic and real networks corroborate the stability andsuperiority of our proposed methods, underscoring their potential for variousgraph learning applications.

LogSpecT: Feasible Graph Learning Model from Stationary Signals with Recovery Guarantees