The log-sum function as a penalty has always been drawing widespread attention in the
field of sparse problems. However, it brings a non-convex, non-smooth and non-Lipschitz …

Conventional compressed sensing theory assumes signals have sparse representations in
a known dictionary. Nevertheless, in many practical applications such as line spectral …

Functional brain network (FBN) has been becoming an increasingly important way to model
the statistical dependence among neural time courses of brain, and provides effective …

The dictionary-aided sparse regression (SR) approach has recently emerged as a promising
alternative to hyperspectral unmixing in remote sensing. By using an available spectral …

In this article, a sparse signal recovery algorithm using Bayesian linear regression with
Cauchy prior (BLRC) is proposed. Utilizing an approximate expectation maximization (AEM) …

The problem of low-rank matrix completion is considered in this paper. To exploit the
underlying low-rank structure of the data matrix, we propose a hierarchical Gaussian prior …

One of the main challenges in block-sparse signal recovery, as encountered in, eg, multi-
antenna mmWave channel models, is block-patterned estimation without knowledge of …

In many practical applications such as direction-of-arrival (DOA) estimation and line spectral
estimation, the sparsifying dictionary is usually characterized by a set of unknown …

In this paper, a 4D tensor model is firstly constructed to explore efficient structural
information and correlations from multi-modal data (both 2D and 3D face data). As the …

In this paper, based on a successively accuracy-increasing approximation of the ℓ 0 norm,
we propose a new algorithm for recovery of sparse vectors from underdetermined …