In recent years, unsupervised feature selection methods have raised considerable interest in
many research areas; this is mainly due to their ability to identify and select relevant features …

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 …

In the past decades, the rapid growth of computer and database technologies has led to the
rapid growth of large-scale datasets. On the other hand, data mining applications with high …

Although demonstrating great success, previous multi-view unsupervised feature selection
(MV-UFS) methods often construct a view-specific similarity graph and characterize the local …

With the ability to exploit the internal structure of data, graph-based models have received a
lot of attention and have achieved great success in multiview subspace clustering for …

One of the most critical challenges in managing complex diseases like COVID-19 is to
establish an intelligent triage system that can optimize the clinical decision-making at the …

As the volume of high-dimensional multi-view data continues to grow, there has been a
significant development in multi-view unsupervised feature selection methods, particularly …

Feature extraction and feature selection have been regarded as two independent
dimensionality reduction methods in most of the existing literature. In this paper, we propose …

Unsupervised band selection aims to select informative spectral bands to preprocess
hyperspectral images (HSIs) without using labels. Traditional band selection methods only …

Feature selection is one of the most important issues in hyperspectral image (HSI)
classification to achieve high correlation between the adjacent bands. The main concern is …