Tensor robust principal component analysis via tensor nuclear norm (TNN) minimization has
been recently proposed to recover the low-rank tensor corrupted with sparse noise/outliers …

In this paper, we address the problem of direction-of-arrival (DOA) estimation using sparse
representation. As the performance of on-grid DOA estimation methods will degrade when …

Sparse recovery has attracted considerable attention in signal processing community these
years, because of its widespread usage in many applications. Though lots of convex and …

Square-root least absolute shrinkage and selection operator (Lasso), a variant of Lasso, has
recently been proposed with a key advantage that the optimal regularization parameter is …

Direction-of-arrival (DOA) estimation is a fundamental problem in many signal processing.
Recently, a variety of sparsity-aware methods have been proposed for DOA estimation. The …

In this paper, we propose to fit a sparse logistic regression model by a weakly convex
regularized nonconvex optimization problem. The idea is based on the finding that a weakly …

In sparse recovery, a sparse signal with nonzero entries is to be reconstructed from a
compressed measurement with (). The pseudonorm has been found to be a sparsity …

Many real-world problems can be modeled as sparse matrix recovery from two-dimensional
(2D) measurements, which is recognized as one of the most important topics in signal …

In this work we propose to fit a sparse logistic regression model by a weakly convex
regularized nonconvex optimization problem. The idea is based on the finding that a weakly …