Search Results for author:
Found 14 papers, 5 papers with code
Greedy Capon Beamformer
We propose greedy Capon beamformer (GBF) for direction finding of narrow-band sources present in the array's viewing field.
Regularized EM algorithm
We show that the theoretical guarantees of convergence hold, leading to better performing EM algorithm for structured covariance matrix models or with low sample settings.
Graph Neural Network Sensitivity Under Probabilistic Error Model
In this paper, we study the effect of a probabilistic graph error model on the performance of the GCNs.
Coupled Feature Learning for Multimodal Medical Image Fusion
The resulting performance and execution times show the competitiveness of the proposed method in comparison with state-of-the-art medical image fusion methods.
Coupled regularized sample covariance matrix estimator for multiple classes
In this work, we consider regularized SCM (RSCM) estimators for multiclass problems that couple together two different target matrices for regularization: the pooled (average) SCM of the classes and the scaled identity matrix.
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 and scale parameters in the linear model.
Linear pooling of sample covariance matrices
We consider the problem of estimating high-dimensional covariance matrices of $K$-populations or classes in the setting where the sample sizes are comparable to the data dimension.
Methodology
A Compressive Classification Framework for High-Dimensional Data
We propose a compressive classification framework for settings where the data dimensionality is significantly higher than the sample size.
M-estimators of scatter with eigenvalue shrinkage
A popular regularized (shrinkage) covariance estimator is the shrinkage sample covariance matrix (SCM) which shares the same set of eigenvectors as the SCM but shrinks its eigenvalues toward its grand mean.
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 regression by utilizing the solution path of $\ell_1$ regularized least-squares (LS) approach (i. e., Lasso).
Methodology Complex Variables Optimization and Control Applications
Sequential adaptive elastic net approach for single-snapshot source localization
This paper proposes efficient algorithms for accurate recovery of direction-of-arrival (DoA) of sources from single-snapshot measurements using compressed beamforming (CBF).
Compressive Regularized Discriminant Analysis of High-Dimensional Data with Applications to Microarray Studies
We propose a modification of linear discriminant analysis, referred to as compressive regularized discriminant analysis (CRDA), for analysis of high-dimensional datasets.
Multichannel sparse recovery of complex-valued signals using Huber's criterion
In this paper, we generalize Huber's criterion to multichannel sparse recovery problem of complex-valued measurements where the objective is to find good recovery of jointly sparse unknown signal vectors from the given multiple measurement vectors which are different linear combinations of the same known elementary vectors.
Robust, scalable and fast bootstrap method for analyzing large scale data
In this paper we address the problem of performing statistical inference for large scale data sets i. e., Big Data.
