298 citations · 934 across the 23 of their papers we have counts for
23 papers
Hyperspectral Image Super-resolution with Deep Priors and Degradation Model Inversion
Xiuheng Wang, Jie Chen, Cédric Richard
To overcome inherent hardware limitations of hyperspectral imaging systems with respect to their spatial resolution, fusion-based hyperspectral image (HSI) super-resolution is attr…
Transient Performance Analysis of the -RLS
Wei Gao, Jie Chen, Cédric Richard +2
The recursive least-squares algorithm with -norm regularization (-RLS) exhibits excellent performance in terms of convergence rate and steady-state error in identif…
Sparse Linear Spectral Unmixing of Hyperspectral images using Expectation-Propagation
Zeng Li, Yoann Altmann, Jie Chen +2
This paper presents a novel Bayesian approach for hyperspectral image unmixing. The observed pixels are modeled by a linear combination of material signatures weighted by their cor…
Projection-based QLP Algorithm for Efficiently Computing Low-Rank Approximation of Matrices
Maboud F. Kaloorazi, Jie Chen
Matrices with low numerical rank are omnipresent in many signal processing and data analysis applications. The pivoted QLP (p-QLP) algorithm constructs a highly accurate approximat…
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…
A Plug-and-Play Priors Framework for Hyperspectral Unmixing
Min zhao, Xiuheng Wang, Jie Chen +1
Spectral unmixing is a widely used technique in hyperspectral image processing and analysis. It aims to separate mixed pixels into the component materials and their corresponding a…
Hyperspectral Unmixing via Nonnegative Matrix Factorization with Handcrafted and Learnt Priors
Min Zhao, Tiande Gao, Jie Chen +1
Nowadays, nonnegative matrix factorization (NMF) based methods have been widely applied to blind spectral unmixing. Introducing proper regularizers to NMF is crucial for mathematic…
Hyperspectral Image Super-Resolution via Deep Prior Regularization with Parameter Estimation
Xiuheng Wang, Jie Chen, Qi Wei +1
Hyperspectral image (HSI) super-resolution is commonly used to overcome the hardware limitations of existing hyperspectral imaging systems on spatial resolution. It fuses a low-res…
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…
Multitask learning over graphs: An Approach for Distributed, Streaming Machine Learning
Roula Nassif, Stefan Vlaski, Cedric Richard +2
The problem of learning simultaneously several related tasks has received considerable attention in several domains, especially in machine learning with the so-called multitask lea…
Hyperspectral Unmixing via Deep Autoencoder Networks for a Generalized Linear-Mixture/Nonlinear-Fluctuation Model
Min Zhao, Mou Wang, Jie Chen +1
Spectral unmixing is an important task in hyperspectral image processing for separating the mixed spectral data pertaining to various materials observed individual pixels. Recently…
Adaptive Parameters Adjustment for Group Reweighted Zero-Attracting LMS
Danqi Jin, Jie Chen, Cedric Richard +1
Group zero-attracting LMS and its reweighted form have been proposed for addressing system identification problems with structural group sparsity in the parameters to estimate. Bot…
Adaptation and learning over networks for nonlinear system modeling
Simone Scardapane, Jie Chen, Cédric Richard
In this chapter, we analyze nonlinear filtering problems in distributed environments, e.g., sensor networks or peer-to-peer protocols. In these scenarios, the agents in the environ…
Multitask diffusion adaptation over networks with common latent representations
Jie Chen, Cédric Richard, Ali H. Sayed
Online learning with streaming data in a distributed and collaborative manner can be useful in a wide range of applications. This topic has been receiving considerable attention in…
Transient performance analysis of zero-attracting LMS
Jie Chen, Cedric Richard, Yingying Song +1
Zero-attracting least-mean-square (ZA-LMS) algorithm has been widely used for online sparse system identification. It combines the LMS framework and -norm regularization to…
Stochastic Behavior of the Nonnegative Least Mean Fourth Algorithm for Stationary Gaussian Inputs and Slow Learning
Jingen Ni, Jian Yang, Jie Chen +2
Some system identification problems impose nonnegativity constraints on the parameters to estimate due to inherent physical characteristics of the unknown system. The nonnegative l…
Diffusion LMS over Multitask Networks
Jie Chen, Cédric Richard, Ali H. Sayed
The diffusion LMS algorithm has been extensively studied in recent years. This efficient strategy allows to address distributed optimization problems over networks in the case wher…
Steady-state performance of non-negative least-mean-square algorithm and its variants
Jie Chen, José Carlos M. Bermudez, Cédric Richard
Non-negative least-mean-square (NNLMS) algorithm and its variants have been proposed for online estimation under non-negativity constraints. The transient behavior of the NNLMS, No…
Multitask Diffusion Adaptation over Networks
Jie Chen, Cédric Richard, Ali. H. Sayed
Adaptive networks are suitable for decentralized inference tasks, e.g., to monitor complex natural phenomena. Recent research works have intensively studied distributed optimizatio…
Convergence analysis of kernel LMS algorithm with pre-tuned dictionary
Jie Chen, Wei Gao, Cédric Richard +1
The kernel least-mean-square (KLMS) algorithm is an appealing tool for online identification of nonlinear systems due to its simplicity and robustness. In addition to choosing a re…
Diffusion LMS for clustered multitask networks
Jie Chen, Cédric Richard, Ali Sayed
Recent research works on distributed adaptive networks have intensively studied the case where the nodes estimate a common parameter vector collaboratively. However, there are many…
Nonlinear unmixing of hyperspectral images using a semiparametric model and spatial regularization
Jie Chen, Cédric Richard, Alfred O. Hero
Incorporating spatial information into hyperspectral unmixing procedures has been shown to have positive effects, due to the inherent spatial-spectral duality in hyperspectral scen…
Online dictionary learning for kernel LMS. Analysis and forward-backward splitting algorithm
Wei Gao, Jie Chen, Cédric Richard +1
Adaptive filtering algorithms operating in reproducing kernel Hilbert spaces have demonstrated superiority over their linear counterpart for nonlinear system identification. Unfort…