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 …

Graph-based feature extraction is an efficient technique for data dimensionality reduction,
and it has gained intensive attention in various fields such as image processing, pattern …

Graph-based sparse feature selection plays an important role in semi-supervised feature
selection. However, traditional graph-based semi-supervised sparse feature selection …

Many methods based on matrix factorization have recently been proposed and achieve
good performance in many practical applications. Latent low-rank representation (LatLRR) …

Neighborhood discriminant index (NDI) is an effective feature selection method for
supervised learning. In reality, it is easy to obtain unlabeled data and is costly to tag them all …

In real-world scenarios, datasets often lack full supervision due to the high cost associated
with acquiring decision labels. Completing datasets by filling in missing labels is essential …

In many real applications, the data are always collected from different types and they are
subjected to obtain partial labeling information of objects. Such data are referred to as …

With the speedy development of network technology, diverse data increase by hundreds of
millions per hour, causing increasing pressure on the acquisition of data labels. Semi …

Due to the rapid development of modern multimedia techniques, high-dimensional image
data are frequently encountered in many image analysis communities, such as clustering …

Feature selection is the process of choosing a subset of pertinent features to use in the
model building during the machine learning and data mining process. Irrelevant and …