Feature selection, as a data preprocessing strategy, has been proven to be effective and
efficient in preparing data (especially high-dimensional data) for various data-mining and …

Alzheimer's disease (AD) is a progressive neurodegenerative disease that often grows in
middle-aged and elderly people with the gradual loss of cognitive ability. Presently, there is …

Feature selection is important in both machine learning and pattern recognition.
Successfully selecting informative features can significantly increase learning accuracy and …

Unsupervised feature selection (UFS) aims to reduce the time complexity and storage
burden, improve the generalization ability of learning machines by removing the redundant …

Multi-label feature selection has grabbed intensive attention in many big data applications.
However, traditional multi-label feature selection methods generally ignore a real-world …

Multi-task learning (MTL) aims to improve the performance of multiple related tasks by
exploiting the intrinsic relationships among them. Recently, multi-task feature learning …

Convex optimization with sparsity-promoting convex regularization is a standard approach
for estimating sparse signals in noise. In order to promote sparsity more strongly than …

In recent years, the enormous demand for computing resources resulting from massive data
and complex network models has become the limitation of deep learning. In the large-scale …

Analysis of incomplete data is a big challenge when integrating large-scale brain imaging
datasets from different imaging modalities. In the Alzheimer's Disease Neuroimaging …

Vector autoregression (VAR) is a fundamental tool for modeling multivariate time series.
However, as the number of component series is increased, the VAR model becomes …