research
∙
08/09/2023
Linear shrinkage of sample covariance matrix or matrices under elliptical distributions: a review
This chapter reviews methods for linear shrinkage of the sample covarian...
share
research
∙
05/07/2023
Affine equivariant Tyler's M-estimator applied to tail parameter learning of elliptical distributions
We propose estimating the scale parameter (mean of the eigenvalues) of t...
research
∙
03/27/2023
Regularized EM algorithm
Expectation-Maximization (EM) algorithm is a widely used iterative algor...
research
∙
01/15/2023
Robust and Sparse M-Estimation of DOA
A robust and sparse Direction of Arrival (DOA) estimator is derived base...
research
∙
03/15/2022
Graph Neural Network Sensitivity Under Probabilistic Error Model
Graph convolutional networks (GCNs) can successfully learn the graph sig...
research
∙
09/03/2021
Regularized tapered sample covariance matrix
Covariance matrix tapers have a long history in signal processing and re...
research
∙
08/18/2021
On the variability of the sample covariance matrix under complex elliptical distributions
We derive the variance-covariance matrix of the sample covariance matrix...
research
∙
02/17/2021
Coupled Feature Learning for Multimodal Medical Image Fusion
Multimodal image fusion aims to combine relevant information from images...
research
∙
11/09/2020
Coupled regularized sample covariance matrix estimator for multiple classes
The estimation of covariance matrices of multiple classes with limited t...
research
∙
08/25/2020
Block-wise Minimization-Majorization algorithm for Huber's criterion: sparse learning and applications
Huber's criterion can be used for robust joint estimation of regression ...
research
∙
08/24/2020
Graph Signal Processing Meets Blind Source Separation
In graph signal processing (GSP), prior information on the dependencies ...
research
∙
08/13/2020
Linear pooling of sample covariance matrices
We consider covariance matrix estimation in a setting, where there are m...
research
∙
06/17/2020
Shrinking the eigenvalues of M-estimators of covariance matrix
A highly popular regularized (shrinkage) covariance matrix estimator is ...
research
∙
05/09/2020
A Compressive Classification Framework for High-Dimensional Data
We propose a compressive classification framework for settings where the...
research
∙
02/12/2020
M-estimators of scatter with eigenvalue shrinkage
A popular regularized (shrinkage) covariance estimator is the shrinkage ...
research
∙
03/20/2019
Modelling Graph Errors: Towards Robust Graph Signal Processing
The first step for any graph signal processing (GSP) procedure is to lea...
research
∙
08/30/2018
Optimal shrinkage covariance matrix estimation under random sampling from elliptical distributions
This paper considers the problem of estimating a high-dimensional (HD) c...
research
∙
06/19/2018
Simultaneous Signal Subspace Rank and Model Selection with an Application to Single-snapshot Source Localization
This paper proposes a novel method for model selection in linear regress...
research
∙
05/19/2018
Sequential adaptive elastic net approach for single-snapshot source localization
This paper proposes efficient algorithms for accurate recovery of direct...
research
∙
04/11/2018
Compressive Regularized Discriminant Analysis of High-Dimensional Data with Applications to Microarray Studies
We propose a modification of linear discriminant analysis, referred to a...
research
∙
04/16/2015
Multichannel sparse recovery of complex-valued signals using Huber's criterion
In this paper, we generalize Huber's criterion to multichannel sparse re...
research
∙
04/09/2015