In this paper, a robust sequential dictionary learning (DL) algorithm is presented. The
proposed algorithm is motivated from the maximum likelihood perspective on dictionary …

Algorithms for learning overcomplete dictionaries for sparse signal representation are mostly
iterative minimization methods that alternate between a sparse coding stage and a …

Understanding pedestrian behaviors is crucial for a safe navigation of self-driving vehicles.
However, pedestrians exhibit a large variety in their motion behaviors that are affected by …

This paper proposes an unrolling learnable approximate message passing recurrent neural
network (called ULAMP-Net) for lensless image reconstruction. By unrolling the optimization …

Accurate model development and efficient representations of multivariate trajectories are
crucial to understanding the behavioral patterns of pedestrian motion. Most of the existing …

Dictionary learning is an important sparse representation algorithm which has been widely
used in machine learning and artificial intelligence. However, for massive data in the big …

Complex-valued signals arise naturally in a wide-range of applications such as radar,
magnetic resonance imaging (MRI), functional MRI (fMRI), remote sensing, communication …

Abstract Analysis of functional magnetic resonance imaging (fMRI) data from multiple
subjects is at the heart of many medical imaging studies, and recently, the approaches …

The distribution of the eigenvalues of a Hermitian matrix (or of a Hermitian matrix pencil)
reveals important features of the underlying problem, whether a Hamiltonian system in …

Dictionary learning algorithms have been successfully applied to a number of signal and
image processing problems. In some applications, however, the observed signals may …