Unsupervised bipartite graph learning has been a hot topic in multi-view clustering, to tackle
the restricted scalability issue of traditional full graph clustering in large-scale applications …

Feature selection has achieved unprecedented success in obtaining sparse discriminative
features. However, the existing methods almost use the-norm constraint on transformation …

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

Due to many unmarked data, there has been tremendous interest in develo**
unsupervised feature selection methods, among which graph-guided feature selection is …

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 …

Feature selection (FS) is essential in machine learning and data mining as it makes
handling high-dimensional data more efficient and reliable. More attention has been paid to …

As graph technology is widely used in unsupervised dimensionality reduction, many
methods automatically construct a full connection graph to learn the structure of data, and …

Feature selection has been widely used in machine learning for a long time. In this paper,
we propose a supervised local sparse discriminative feature selection method named …

Graph-based semi-supervised dimension reduction can use the inherent graph structure of
samples to propagate label information, and has become a hot research field in machine …

Adaptive graph-based representation and learning methods have received extensive
attention due to their good performance in supervised and unsupervised learning tasks …