The heavy-tailed distributions of corrupted outliers and singular values of all channels in low-
level vision have proven effective priors for many applications such as background …

Regression analysis based methods have shown strong robustness and achieved great
success in face recognition. In these methods, convex-norm and nuclear norm are usually …

For robust face recognition tasks, we particularly focus on the ubiquitous scenarios where
both training and testing images are corrupted due to occlusions. Previous low-rank based …

In this work, we focus on studying the differentiable relaxations of several linear regression
problems, where the original formulations are usually both nonsmooth with one nonconvex …

Even after decades of software engineering research, complex computer systems still fail,
primarily due to nondeterministic bugs that are typically resolved by rebooting. Conceding …

This paper proposes a novel discriminative regression method, called adaptive locality
preserving regression (ALPR) for classification. In particular, ALPR aims to learn a more …

Face recognition has been active research in the security domain. Human face recognition
gains importance for developing a secured environment for the organization and also …

In this work we address the problem of real-time dynamic medical (MRI and X-Ray CT)
image reconstruction from parsimonious samples (Fourier frequency space for MRI and …

In this paper, we consider the robust face recognition problem via iterative re-constrained
group sparse classifier (IRGSC) with adaptive weights learning. Specifically, we propose a …

Accompanied with the increasing popularity of linear regression approaches, most of the
existing minimization problems are related with several convex measurements, eg,//-norm of …