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 deals with problem of blind identification of a graph filter andits sparse input signal, thus broadening the scope of classical blinddeconvolution of temporal and spatial signals to irregular graph domains. Whilethe observations are bilinear functions of the unknowns, a mild requirement oninvertibility of the filter enables an efficient convex formulation, withoutrelying on matrix lifting that can hinder applicability to large graphs. On topof scaling, it is argued that (non-cyclic) permutation ambiguities may arisewith some particular graphs. Deterministic sufficient conditions under whichthe proposed convex relaxation can exactly recover the unknowns are stated,along with those guaranteeing identifiability under the Bernoulli-Gaussianmodel for the inputs. Numerical tests with synthetic and real-world networksillustrate the merits of the proposed algorithm, as well as the benefits ofleveraging multiple signals to aid the (blind) localization of sources ofdiffusion.

Blind Identification of Invertible Graph Filters with Multiple Sparse Inputs