JSTOR Daily

Generalized Least Squares with an Estimated Variance Covariance Matrix

The paper discusses why certain commonly used two-step procedures give estimators which are asymptotically less efficient than the maximum likelihood estimator when there are lagged dependent ...
IEEE

A Lightweight Riemannian Covariance Matrix Convolutional Network for PolSAR Image Classification

Abstract: Recently, deep learning methods have achieved superior performance for polarimetric synthetic aperture radar (PolSAR) image classification. Existing deep learning methods learn PolSAR data ...
IEEE

Regularized Linear Discriminant Analysis Using a Nonlinear Covariance Matrix Estimator

Abstract: Linear discriminant analysis (LDA) is a widely used technique for data classification. The method offers adequate performance in many classification problems, but it becomes inefficient when ...
GitHub

Understanding Matrix Function Normalizations in Covariance Pooling through the Lens of Riemannian Geometry

Figure 2: The distances under Power Euclidean Metric (PEM) and Log-Euclidean Metric (LEM) are significantly different (256 x 256 SPD matrices). This is the official code for our ICLR 2025 publication: ...