In unsupervised feature selection, the clustering label matrix has the ability to distinguish
between projection clusters. However, the latent geometric structure of the clustering labels …

Feature selection uses the hierarchical dependency information provided by multi-
granularity knowledge to identify the most relevant features for a given task. Most of these …

Unsupervised feature selection has gained considerable attention for extracting valuable
features from unlabeled datasets. Existing approaches typically rely on sparse mapping …

Large amounts of high-dimensional unlabeled data typically contain only a small portion of
truly effective information. Consequently, the issue of unsupervised feature selection …

Before conducting unsupervised feature selection, it is usually assumed that these data are
independent of each other. On the contrary, real data will influence each other. Therefore …

Abstract Tolerance Rough Set (TRS) theory is commonly employed for feature selection with
incomplete data. However, TRS has limitations such as ignoring uncertainty, which often …

Unsupervised feature selection (UFS) is widely applied in machine learning and pattern
recognition. However, most of the existing methods only consider a single sparsity, which …

Principal Component Analysis (PCA) is a feature extraction technique that has been
successfully applied in the data preprocessing stage of various algorithms. However, most …