Abstract Two Dimensional Linear Discrimination Analysis (2DLDA) is an effective feature
extraction approach for face recognition, which manipulates on the two dimensional image …

Graph embedding based learning method plays an increasingly significant role on
dimensionality reduction (DR). However, the selection to neighbor parameters of graph is …

This paper presents a median–mean line based discriminant analysis (MMLDA) technique
for dimensionality reduction. Taking the negative effect on the class-mean caused by outliers …

In this paper, we propose a new feature extraction method—parameterized direct linear
discriminant analysis (PD-LDA) for small sample size problems. Similar to direct LDA (D …

PCA plus LDA is a popular framework for linear discriminant analysis (LDA) in high
dimensional and singular case. In this paper, we focus on building a theoretical foundation …

Locally Linear Embedding (LLE), which has recently emerged as a powerful face feature
descriptor, suffers from a limitation. That is class-specific information of data is lacked of …

In classification of matrix-variate data, two-directional linear discriminant analysis (2DLDA)
methods extract discriminant features while preserving and utilizing the matrix structure …

In this paper, we present a new statistical projection-based face recognition method, called
Bilinear Discriminant Analysis (BDA). The proposed technique effectively combines two …

Two‐dimensional linear discriminant analysis (2DLDA) is a widely applied extension of LDA
that can cope with matrix input samples directly. However, its construction is based on a …

When handling pattern classification problem such as face recognition and digital
handwriting identification, image data is always represented to high dimensional vectors …