Feature selection is a highly regarded research area in the field of data mining, as it
significantly enhances the efficiency and performance of high-dimensional data analysis by …

Masked graph autoencoder (MGAE) has emerged as a promising self-supervised graph pre-
training (SGP) paradigm due to its simplicity and effectiveness. However, existing efforts …

As data with diverse representations become highdimensional, multi-view unsupervised
feature selection has been an important learning paradigm. Generally, existing methods …

It is a challenging task to select the informative features that can maintain the manifold
structure in the original feature space. Many unsupervised feature selection methods still …

Unsupervised feature selection (UFS), which selects the most important feature subset and
eliminates the unnecessary information for the upcoming data analysis, is a significant …

Unsupervised feature selection (UFS) aims to learn an indicator matrix relying on some
characteristics of the high-dimensional data to identify the features to be selected. However …

Graph-based unsupervised feature selection methods have successfully processed high-
dimensional data since they can effectively preserve data structure information. However …

Band selection (BS) is an effective dimensionality reduction technique for hyperspectral
images. Although many relevant methods have been proposed, they often only focus on the …

To tackle the high-dimensional data with multiple representations, multi-view unsupervised
feature selection has emerged as a significant learning paradigm. However, previous …

This paper presents a novel framework for continual feature selection (CFS) in data
preprocessing, particularly in the context of an open and dynamic environment where …