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20132022
most citedDiffusion LMS with Communication Delays: Stability and Performance Analysis

30 citations · 54 across the 7 of their papers we have counts for

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8 papers · 1 filter

eess.SP2021

Graph topology inference with derivative-reproducing property in RKHS: algorithm and convergence analysis

Mircea Moscu, Ricardo A. Borsoi, Cédric Richard +1

In many areas such as computational biology, finance or social sciences, knowledge of an underlying graph explaining the interactions between agents is of paramount importance but…

eess.SP2021

Transient Theoretical Analysis of Diffusion RLS Algorithm for Cyclostationary Colored Inputs

Wei Gao, Jie Chen, Cédric Richard

Convergence of the diffusion RLS (DRLS) algorithm to steady-state has been extensively studied in the literature, whereas no analysis of its transient convergence behavior has been…

eess.SP2020

Coupled Tensor Decomposition for Hyperspectral and Multispectral Image Fusion with Inter-Image Variability

Ricardo Augusto Borsoi, Clémence Prévost, Konstantin Usevich +3

Coupled tensor approximation has recently emerged as a promising approach for the fusion of hyperspectral and multispectral images, reconciling state of the art performance with st…

eess.SP202030 cited

Diffusion LMS with Communication Delays: Stability and Performance Analysis

Fei Hua, Roula Nassif, Cédric Richard +2

We study the problem of distributed estimation over adaptive networks where communication delays exist between nodes. In particular, we investigate the diffusion Least-Mean- Square…

eess.SP20202 cited

Affine Combination of Diffusion Strategies over Networks

Danqi Jin, Jie Chen, Cedric Richard +2

Diffusion adaptation is a powerful strategy for distributed estimation and learning over networks. Motivated by the concept of combining adaptive filters, this work proposes a comb…

eess.SP20191 cited

Online Distributed Learning over Graphs with Multitask Graph-Filter Models

Fei Hua, Roula Nassif, Cédric Richard +2

In this work, we are interested in adaptive and distributed estimation of graph filters from streaming data. We formulate this problem as a consensus estimation problem over graphs…