Least squares regression (LSR) and its extended methods are widely used for image
classification. However, these LSR-based methods do not consider the importance of global …

Biomedical signals are frequently contaminated by colored noise; consequently, noise
recognition and reduction are critical to biomedical systems. Conventional techniques have …

It is essential to define more convincing and applicable classifiers for small datasets. In this
paper, a minimum reconstruction error-based K-nearest neighbors (K-NN) classifier is …

Discriminative least-squares regression (DLSR) has been shown to achieve promising
performance in multiclass image classification tasks. Its key idea is to force the regression …

Most existing shallow semi-supervised domain adaptation (SSDA) algorithms are based
mainly on the framework adopting the maximum mean discrepancy (MMD) criterion, which is …

High-dimensional data present significant challenges such as inadequate sample size,
abundance of noise, and the curse of dimensionality, which make many traditional …

Numerous methods have exploited projection learning to extract low-dimensional features
for image classification. Some projection learning methods integrate low-rank matrix …

Due to the large-scale and multiscale natures of social media data, sparse representation
(SR) learning methods are widely followed. However, there are three problems associated …

In this paper, we propose a novel embedded feature selection method, which is able to
select the informative and discriminative features with the underlying submanifolds of data in …

In recent years, analysis dictionary learning (ADL) model has attracted much attention from
researchers, owing to its scalability and efficiency in representation-based classification …