We address the problem of estimating a sparse low-rank matrix from its noisy observation.
We propose an objective function consisting of a data-fidelity term and two parameterized …

Image denoising is one of the most important tasks in image processing. In this paper, we
study image denoising methods by using similar patches which have low-rank covariance …

The estimation of the covariance matrix is an important topic in the field of multivariate
statistical analysis. In this paper, we propose a new estimator, which is a convex …

In recent years, there has been a growing interest in mathematical models leading to the
minimization, in a symmetric matrix space, of a Bregman divergence coupled with a …

The covariance matrix estimation plays an important role in portfolio optimization and risk
management. It is well-known that portfolio is essentially a convex quadratic programming …

There are two key challenges when using a linear discriminant analysis in the high-
dimensional setting, including singularity of the covariance matrix and difficulty of …

The rank minimization problem with affine constraints is widely applied in the fields of
control, system identification, and machine learning, and attracted much attention and well …

This paper is concerned with the structured simultaneous low-rank and sparse recovery,
which can be formulated as the rank and zero-norm regularized least squares problem with …

Background: Low-rank approximation is used to interpret the features of a correlation matrix
using visualization tools; however, a low-rank approximation may result in an estimation that …

The development and massification of medical imaging and clinical followup databases
open up new perspectives for understanding complex phenomena such as ageing or …