This work introduces a regularized graph filtering scheme based on fractional lower order moments, coupled with distinct adjacency matrices.

To address the denoising of graph-structured signals, under impulsive noise conditions, this work introduces a regularized graph filtering scheme based on ...

To address the denoising of graph-structured signals, under impulsive noise conditions, this work introduces a regularized graph filtering scheme based on ...

To address the denoising of graph-structured signals, under impulsive noise conditions, this work introduces a regularized graph filtering scheme based on ...

One of the most important aspects in GSP theory is the computation of an appropriate adjacency matrix that best represents the interconnectivity relations ...

A study on the effect of distinct adjacency matrices for graph signal denoising, in: Proc. 20th BioInformatics And BioEngineering Conference (BIBE '20) ...

This work proposes a new method for suppressing the effects of heavy-tailed noise in EEG recordings by first modelled by means of graph representations, ...

2023. A study on the effect of distinct adjacency matrices for graph signal denoising. A Pentari, G Tzagkarakis, K Marias, P Tsakalides. 2020 IEEE 20th ...

A Study on the Effect of Distinct Adjacency Matrices for Graph Signal Denoising. Conference Paper. Oct 2020. Anastasia Pentari ...

Tsakalides, "A Study on the Effect of Distinct Adjacency Matrices for Graph Signal Denoising," in Proc. 20th IEEE International Conference on BioInformatics ...