As more features are introduced in pattern recognition and machine learning applications,
feature selection remains a critically important task to find the most compact representation …

One of the challenges in data mining is the dimensionality of data, which is often very high
and prevalent in many domains, such as text categorization and bio-informatics. The high …

Feature selection has aroused considerable research interests during the last few decades.
Traditional learning-based feature selection methods separate embedding learning and …

Unsupervised feature selection is a vital yet challenging topic for effective data learning.
Recently, 2-D feature selection methods show good performance on image analysis by …

Feature selection is an important preprocessing step for dealing with high dimensional data.
In this paper, we propose a novel unsupervised feature selection method by embedding a …

In recent decades, unsupervised feature selection methods have become increasingly
popular. Nevertheless, most of the existing unsupervised feature selection methods suffer …

Feature self-representation has become the backbone of unsupervised feature selection,
since it is almost insensitive to noise data. However, feature selection methods based on …

In the past decade, various sparse learning based unsupervised feature selection methods
have been developed. However, most existing studies adopt a two-step strategy, ie …

Unsupervised feature selection is an important problem, especially for high‐dimensional
data. However, until now, it has been scarcely studied and the existing algorithms cannot …

Feature selection is viewed as an important preprocessing step for pattern recognition,
machine learning, and data mining. Neighborhood is one of the most important concepts in …