Recently, feature selection and dimensionality reduction have become fundamental tools for
many data mining tasks, especially for processing high-dimensional data such as gene …

Driving fatigue seriously threatens traffic safety. In our work, the multifractal detrended
fluctuation analysis (MF-DFA) method is proposed to detect driver fatigue caused by driving …

This survey aims at providing a state-of-the-art overview of feature selection and fusion
strategies, which select and combine multi-view features effectively to accomplish …

Linear discriminant analysis (LDA) is a very popular supervised feature extraction method
and has been extended to different variants. However, classical LDA has the following …

Dimension reduction methods for microarray data: a review Page 1 AIMS Bioengineering, 4(2):
179-197. DOI: 10.3934/bioeng.2017.2.179 Received: 27 November 2016 Accepted: 01 …

Feature selection (FS), which identifies the relevant features in a data set to facilitate
subsequent data analysis, is a fundamental problem in machine learning and has been …

Feature selection (FS) is an important component of many pattern recognition tasks. In these
tasks, one is often confronted with very high-dimensional data. FS algorithms are designed …

By leveraging the labeled data samples of the source domain to learn the unlabeled data
samples of the target domain, unsupervised domain adaptation (DA) has achieved …

In this paper, we address the problem of unsupervised domain transfer learning in which no
labels are available in the target domain. We use a transformation matrix to transfer both the …

Feature selection is viewed as an important preprocessing step for pattern recognition,
machine learning, and data mining. Neighborhood is one of the most important concepts in …