11 citations · 28 across the 9 of their papers we have counts for
5 papers · 1 filter
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…
Block-wise Minimization-Majorization algorithm for Huber's criterion: sparse learning and applications
Esa Ollila, Ammar Mian
Huber's criterion can be used for robust joint estimation of regression and scale parameters in the linear model. Huber's (Huber, 1981) motivation for introducing the criterion ste…
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…
A Compressive Classification Framework for High-Dimensional Data
Muhammad Naveed Tabassum, Esa Ollila
We propose a compressive classification framework for settings where the data dimensionality is significantly higher than the sample size. The proposed method, referred to as compr…
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…