Considering the labeled samples may be difficult to obtain because they require human
annotators, special devices, or expensive and slow experiments. Semi-supervised learning …

A graph structure is a powerful mathematical abstraction, which can not only represent
information about individuals but also capture the interactions between individuals for …

Dimensionality reduction (DR) is an important way of improving the classification accuracy of
a hyperspectral image (HSI). Graph learning, which can effectively reveal the intrinsic …

Feature extraction and feature selection have been regarded as two independent
dimensionality reduction methods in most of the existing literature. In this paper, we propose …

In the clinical study of Alzheimer's Disease (AD) with neuroimaging data, it is challenging to
identify the progressive Mild Cognitive Impairment (pMCI) subjects from the stableMCI …

This paper develops a new dimensionality reduction method, named biomimetic
uncorrelated locality discriminant projection (BULDP), for face recognition. It is based on …

Amidst advancements in feature extraction techniques, research on multi-view multi-label
classifications has attracted widespread interest in recent years. However, real-world …

Linear Discriminant Analysis (LDA) is one of the most successful supervised dimensionality
reduction methods and has been widely used in many real-world applications. However, l 2 …

As an effective image clustering tool, low-rank representation (LRR) can capture the intrinsic
representation of the observed samples. However, firstly, the good representation does not …

Data-driven fault diagnosis techniques have been widely used in industrial processes.
However, facing a large amount of high-dimensional, nonlinear, and strongly coupled …