Model-based analysis tools, built on assumptions and simplifications, are difficult to handle
smart grids with data characterized by volume, velocity, variety, and veracity (ie, 4Vs data) …

Feature selection aims to gain relevant features for improved classification performance and
remove redundant features for reduced computational cost. How to balance these two …

In imbalanced learning, most standard classification algorithms usually fail to properly
represent data distribution and provide unfavorable classification performance. More …

Low-rank tensor completion (LRTC) has successfully been applied to a wide range of real-
world problems. Despite the broad, successful applications, existing LRTC methods may …

Biomarker assisted preclinical/early detection and intervention in Alzheimer's disease (AD)
may be the key to therapeutic breakthroughs. One of the presymptomatic hallmarks of AD is …

Principal component analysis (PCA) is widely applied in various areas, one of the typical
applications is in face. Many versions of PCA have been developed for face recognition …

Many graph embedding methods are developed for dimensionality reduction (DR) of
hyperspectral image (HSI), which only use spectral features to reflect a point-to-point …

This paper presents a generalized incremental Laplacian Eigenmaps (GENILE), a novel
online version of the Laplacian Eigenmaps, one of the most popular manifold-based …

Unsupervised feature selection plays a dominant role in the process of high-dimensional
and unlabeled data. Conventional spectral-based unsupervised feature selection methods …

Hyperspectral image (HSI) provides both spatial structure and spectral information for
classification, but many traditional methods simply concatenate spatial features and spectral …