11 citations · 28 across the 9 of their papers we have counts for
12 papers · 1 filter
Regularized tapered sample covariance matrix
Esa Ollila, Arnaud Breloy
Covariance matrix tapers have a long history in signal processing and related fields. Examples of applications include autoregressive models (promoting a banded structure) or beamf…
Coupled regularized sample covariance matrix estimator for multiple classes
Elias Raninen, Esa Ollila
The estimation of covariance matrices of multiple classes with limited training data is a difficult problem. The sample covariance matrix (SCM) is known to perform poorly when the…
Shrinking the eigenvalues of M-estimators of covariance matrix
Esa Ollila, Daniel P. Palomar, Frédéric Pascal
A highly popular regularized (shrinkage) covariance matrix estimator is the shrinkage sample covariance matrix (SCM) which shares the same set of eigenvectors as the SCM but shrink…
M-estimators of scatter with eigenvalue shrinkage
Esa Ollila, Daniel P. Palomar, Frederic Pascal
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 eigenval…
Optimal shrinkage covariance matrix estimation under random sampling from elliptical distributions
Esa Ollila, Elias Raninen
This paper considers the problem of estimating a high-dimensional (HD) covariance matrix when the sample size is smaller, or not much larger, than the dimensionality of the data, w…
Simultaneous Signal Subspace Rank and Model Selection with an Application to Single-snapshot Source Localization
Muhammad Naveed Tabassum, Esa Ollila
This paper proposes a novel method for model selection in linear regression by utilizing the solution path of regularized least-squares (LS) approach (i.e., Lasso). This m…