This paper proposes a framework for optimizing cost functions of orthonormal basis learning
problems, such as principal component analysis (PCA), subspace recovery, orthogonal …

Detection of buried objects such as pipes using a Ground Penetrating Radar (GPR) is
intricate for three main reasons. First, noise is important in the resulting image because of …

This paper addresses adaptive detection of a range distributed target in the presence of
dominant heterogeneous clutter, which is (possibly) low-rank and lies in a known subspace …

This paper addresses the problem of the clutter subspace projector estimation in the context
of a disturbance composed of a low rank heterogeneous (Compound Gaussian) clutter and …

Modern data are increasingly both high-dimensional and heteroscedastic. This paper
considers the challenge of estimating underlying principal components from high …

Principal component analysis (PCA) is a classical and ubiquitous method for reducing data
dimensionality, but it is suboptimal for heterogeneous data that are increasingly common in …

This paper studies a statistical model for heteroscedastic (ie, power fluctuating) signals
embedded in white Gaussian noise. Using the Riemannian geometry theory, we propose an …

This paper addresses the problem of robust covariance matrix (CM) estimation in the context
of a disturbance composed of a low rank (LR) heterogeneous clutter plus an additive white …

In this paper, we consider the problem of low dimensional signal subspace estimation in a
Bayesian context. We focus on compound Gaussian signals embedded in white Gaussian …

This article derives a new change detector for multivariate synthetic aperture radar (SAR)
image time series (ITS). Classical statistical change detection methodologies based on …