Curse of dimensionality is known as big challenges in data mining, pattern recognition,
computer vison and machine learning in recent years. Feature selection and feature …

Feature selection (FS) aims to eliminate redundant features and choose the informative
ones. Since labeled data are not always easily available and abundant unlabeled data are …

Unsupervised feature selection robust to many outliers is a challenging task. The crucial
difficulty is learning a robust subspace, which preserves local structure. The most common …

Due to its efficiency and flexibility, many unsupervised feature selection models based on
dictionary learning have been proposed to select prominent features for improving the …

Unsupervised feature selection for subspace learning is an effective dimensionality
reduction strategy whose essence lies in representing the original space with a lower …

Parkinson's disease (PD) is a neurodegenerative condition common among the elderly, with
optimal treatment ideally administered in its early stages. Given the high rate of …

The vibration signal of rolling bearings is a nonlinear and non-stationary signal, which is
affected by the working condition change and background noise, and the reliability of …

Feature extraction can effectively alleviate the curse of dimensionality on high-dimensional
data. Contrastive learning, as a self-supervised learning method, has garnered widespread …

Hierarchical feature selection is considered an effective technique to reduce the
dimensionality of data with complex hierarchical label structures. Incorrect labels are a …

With the development of intelligent technology, data in practical applications show
exponential growth in quantity and scale. Extracting the most distinguished attributes from …