Unsupervised feature selection attempts to select a small number of discriminative features
from original high-dimensional data and preserve the intrinsic data structure without using …

Ant colony optimization (ACO) is widely used in feature selection owing to its excellent
global/local search capabilities and flexible graph representation. However, the current ACO …

Hyperspectral image (HSI) contains a large number of spatial-spectral information, which
will make the traditional classification methods face an enormous challenge to discriminate …

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 approximation of low-order information-theoretic terms for feature selection approaches
has achieved success in addressing high-dimensional multi-label data. However, three …

This paper proposes a new unsupervised spectral feature selection method to preserve both
the local and global structure of the features as well as the samples. Specifically, our method …

We propose a neural network-based feature selection (FS) scheme that can control the level
of redundancy in the selected features by integrating two penalties into a single objective …

Feature selection plays a critical role in pattern recognition. Feature selection aims to
eliminate irrelevant and redundant features. A drawback of traditional feature selection …

Unsupervised spectral feature selection (USFS) methods could output interpretable and
discriminative results by embedding a Laplacian regularizer in the framework of sparse …

Feature selection plays a critical role in classification problems. Feature selection methods
intend to retain relevant features and eliminate redundant features. This work focuses on …