This brief investigates distributed and adaptive estimation of streaming data based nonlinear
graph filters. To begin with, a new distributed polynomial filter for nonlinear graphs is …

In this brief, we propose a robust proportionate Recursive Least Square (RLS) algorithm to
address the problem of adaptive graph signal recovery in the presence of impulsive noise. In …

Graph signal processing (GSP) has two methods including graph Fourier transform (GFT)
and graph shift-operator (GSO) for dealing with the graph signal used for adaptive filtering …

This paper proposes a robust adaptive algorithm for smooth graph signal recovery which is
based on generalized correntropy. A proper cost function is defined, which takes the …

In this paper, we address the challenge of signal reconstruction on Cartesian product
graphs, which frequently arises in applications where node-level data may be corrupted by …

In this paper, the graph signal recovery problem is addressed by employing an aggregation
of samples in the vertex domain and the Fourier graph transform domain. The statistical …

In this paper, we present a novel approach for the reconstruction of sparse graph signals
using a distributed adaptive thresholding recursive least squares algorithm. Our proposed …

In this study, we focus on an adaptive filtering algorithm that utilizes variable step-size and
incorporates graph filter models within the realm of graph signal processing. The algorithm …

In this work, we introduce an extended gradient method that employs the gradients of the
preceding two iterates to construct the search direction for the purpose of solving the …

In this paper, we propose a novel compressed distributed auxiliary particle filter that uses
graph theory (CDAPF-GT) to reduce the communication cost and improve the estimation …