Recently, many algorithms based on locally graph embedding are proposed for dimensional
reduction in nonlinear data. However, these algorithms are not effective when dealing with …

How to capture distinctive features from facial images when there are large variations in
illumination, poses, and expressions is important for the face recognition problems. This …

This paper presents a new method for image feature extraction, namely, the fuzzy 2D
discriminant locality preserving projections (F2DDLPP) based on the 2D discriminant locality …

In graph embedding based learning algorithms, how to construct the local neighborhood
graphs in applications is a difficult but important problem. In this paper, we propose a novel …

Many classical feature extraction and dimensionality reduction algorithms, such as linear
algorithm principal component analysis (PCA) and nonlinear algorithm local linear …

In recent years, many face image feature extraction and dimensional reduction algorithms
have been proposed for linear and nonlinear data, such as local‐based graph embedding …

In two-dimensional local graph embedding discriminant analysis, the intrinsic graph
characterizes the intraclass compactness and connects each data point with its neighboring …

In pattern recognition, feature extraction techniques have been widely employed to reduce
the dimensionality of high‐dimensional data. In this paper, we propose a novel feature …