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

Graph-based sparse feature selection plays an important role in semi-supervised feature
selection, which greatly improves the performance of feature selection. However, most …

We propose a novel discriminative feature learning method via Max-Min Ratio Analysis
(MMRA) for exclusively dealing with the long-standing “worst-case class separation” …

Principal component analysis (PCA) is one of the most successful unsupervised subspace
learning methods and has been used in many practical applications. To deal with the …

To utilize both labeled data and unlabeled data in real-world applications, semi-supervised
learning is widely used as an effective technique. However, most semi-supervised methods …

As one of the most popular supervised dimensionality reduction methods, linear discriminant
analysis (LDA) has been widely studied in machine learning community and applied to …

Feature selection is one of the important topics of machine learning, and it has a wide range
of applications in data preprocessing. At present, feature selection based on-norm …

In this article, we propose a new unsupervised feature selection method named pseudo-
label guided structural discriminative subspace learning (PSDSL). Unlike the previous …

Electroencephalogram (EEG) signals are widely used in the field of emotion recognition
since it is resistant to camouflage and contains abundant physiological information …

Due to the ability to leverage information from both unlabeled and labeled data, semi-
supervised classification has found extensive applications in various practical scenarios …