Group lasso is widely used to enforce the structural sparsity, which achieves the sparsity at
inter-group level. In this paper, we propose a new formulation called``exclusive group …

Recently, exclusive lasso has demonstrated its promising results in selecting discriminative
features for each class. The sparsity is enforced on each feature across all the classes via l …

Abstract Sparse-Group Lasso (SGL) has been shown to be a powerful regression technique
for simultaneously discovering group and within-group sparse patterns by using a …

Feature selection aims to identify the most informative features for a compact and accurate
data representation. As typical supervised feature selection methods, Lasso and its variants …

In this article, we propose a novel feature selection approach, named unsupervised feature
selection with constrained-norm (row-sparsity constrained) and optimized graph (RSOGFS) …

We propose an embedded/integrated feature selection method based on neural networks
with Group Lasso penalty. Group Lasso regularization is considered to produce sparsity on …

The group Lasso is an extension of the Lasso for feature selection on (predefined) non-
overlapping groups of features. The non-overlapping group structure limits its applicability in …

Classification with a sparsity constraint on the solution plays a central role in many high
dimensional signal processing applications. In some cases, the features can be grouped …

Most state-of-the-art feature selection methods tend to overlook the structural relationship
between a pair of samples associated with each feature dimension, which may encapsulate …

Lasso is a widely used regression technique to find sparse representations. When the
dimension of the feature space and the number of samples are extremely large, solving the …