Linear discriminant analysis (LDA) as a classical supervised dimensionality reduction
method has shown powerful capability in various image classification tasks. The purpose of …

In this paper, we propose a robust linear discriminant analysis (RLDA) through
Bhattacharyya error bound optimization. RLDA considers a nonconvex problem with the L 1 …

Objective The standard treatment for patients with locally advanced gastric cancer has relied
on perioperative radio-chemotherapy or chemotherapy and surgery. The aim of this study …

A blind watermarking algorithm is proposed, which is based on the Discrete Cosine
Transform (DCT) method. It uses Two-Dimensional Linear Discriminant Analysis (2DLDA) …

The global prevalence of childhood and adolescent obesity is a major concern due to its
association with chronic diseases and long-term health risks. Artificial intelligence …

Over the past two decades, matrix-based or bilinear discriminant analysis (BLDA) methods
have received much attention. However, it has been reported that the traditional vector …

In this paper, to address driving safety under emergency situations, we investigated the
neural signatures of emergency braking and soft braking of drivers and proposed …

Dimension reduction is a critical technology for high-dimensional data processing, where
Linear Discriminant Analysis (LDA) and its variants are effective supervised methods …

Principal component analysis (PCA) is a popular dimension reduction technique for vector
data. Factored PCA (FPCA) is a probabilistic extension of PCA for matrix data, which can …

Locality preserving projections (LPP) can preserve the local structure of the datasets
effectively. However, it is not capable of separating the samples that are close to each other …