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 …

Feature selection (FS), which identifies the relevant features in a data set to facilitate
subsequent data analysis, is a fundamental problem in machine learning and has been …

Subspace learning decreases the dimensions for high-dimensional data by projecting the
original data into a low-dimensional subspace, as well as preserving the similarity among …

As one of the important dimensionality reduction techniques, unsupervised feature selection
(UFS) has enjoyed amounts of popularity over the last few decades, which can not only …

In recent years, Botnet has become one of the most dreadful type of malicious entity.
Because of the hidden and carrying capacity of Botnet, the detection task has become a real …

Band selection is one of the most effective methods to reduce the band redundancy of
hyperspectral images (HSIs). Most existing band selection methods tend to regard each …

Multi-view learning has become a popular research topic in recent years, but research on
the cross-application of classic multi-label classification and multi-view learning is still in its …

Unsupervised feature selection is an important machine learning task and thus attracts
increasingly more attention. However, due to the absence of labels, unsupervised feature …

In unsupervised feature selection, the relationship between pseudo-labels is often ignored,
and the interconnection information between the data is not fully utilized. In order to solve …

In many real-world unsupervised learning applications, given data with balanced
distribution, that is, there are an approximately equal number of instances in each class, we …