A taxonomy of unsupervised feature selection methods including their pros, cons, and challenges

Abstract

In pattern recognition, statistics, machine learning, and data mining, feature or attribute selection is a standard dimensionality reduction method. The goal is to apply a set of rules to select essential and relevant features from the original dataset. In recent years, unsupervised feature selection approaches have garnered significant attention across various research fields. This study presents a well-organized summary of the latest and most effective unsupervised feature selection techniques in the scientific literature. We introduce a taxonomy of these strategies, elucidating their significant features and underlying principles. Additionally, we outline the pros, cons, challenges, and practical applications of the broad categories of unsupervised feature selection approaches reviewed in the literature. Furthermore, we conducted a comparison of several state-of-the-art unsupervised feature selection methods through experimental analysis.