In this paper, we survey and compare different algorithms that, given an overcomplete
dictionary of elementary functions, solve the problem of simultaneous sparse signal …

In image analysis, the images are often represented by multiple visual features (also known
as multiview features), that aim to better interpret them for achieving remarkable …

DC (difference of convex functions) programming and DC algorithm (DCA) are powerful
tools for nonsmooth nonconvex optimization. This field was created in 1985 by Pham Dinh …

Dimensionality reduction is a very important topic in machine learning. It can be generally
classified into two categories: feature selection and subspace learning. In the past decades …

We present two algorithms to solve the Group Lasso problem (Yuan and Lin in, JR Stat Soc
Ser B (Stat Methodol) 68 (1): 49–67, 2006). First, we propose a general version of the Block …

Saliency mechanism has been considered crucial in the human visual system and helpful to
object detection and recognition. This paper addresses a novel feature-based model for …

Motivation: Recent advances in brain imaging and high-throughput genotyping techniques
enable new approaches to study the influence of genetic and anatomical variations on brain …

Currently sparse signal reconstruction gains considerable interest and is applied in many
fields. In this paper, a similarity induced by joint sparse representation is designed to …

With a small number of training range cells, sparse recovery (SR)-based space–time
adaptive processing (STAP) methods can help to suppress clutter and detect targets …

We present two model-based neural network architectures purposed for sporadic user
detection and channel estimation in massive machine-type communications. In the scenario …