The fused least absolute shrinkage and selection operator (LASSO) simultaneously
pursuing the joint sparsity of coefficients and their successive differences has attracted …

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-type feature selection has been demonstrated to be effective in handling high
dimensional data. Most existing Lasso-type models over emphasize the sparsity and …

LASSO (Least Absolute Shrinkage and Selection Operator) is a useful tool to achieve the
shrinkage and variable selection simultaneously. Since LASSO uses the L 1 penalty, the …

Over recent years, the state-of-the-art lasso and adaptive lasso have aquired remarkable
consideration. Unlike the lasso technique, adaptive lasso welcomes the variables' effects in …

Fused Lasso is a popular regression technique that encodes the smoothness of the data. It
has been applied successfully to many applications with a smooth feature structure …

In many statistical modeling problems, such as classification and regression, it is common to
encounter sparse and blocky coefficients. Sparse fused Lasso is specifically designed to …

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 …

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 …

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 …