In the last decade, many variants of classical linear discriminant analysis (LDA) have been
developed to tackle the under-sampled problem in face recognition. However, choosing the …

Linear discriminant regression classification (LDRC) was presented recently in order to
boost the effectiveness of linear regression classification (LRC). LDRC aims to find a …

To address two problems, namely nonlinear problem and singularity problem, of linear
discriminant analysis (LDA) approach in face recognition, this paper proposes a novel …

Linear discriminant analysis (LDA) is one of the most popular techniques for extracting
features in face recognition. LDA captures the global geometric structure. However, local …

There are two fundamental problems with the linear discriminant analysis (LDA) for face
recognition. First one is LDA is not stable because of the small training sample size problem …

In recent years, face recognition in the unconstrained environment has attracted increasing
attentions, and a few methods have been evaluated on the Labeled Faces in the Wild (LFW) …

Ridge regression (RR) for classification is a regularized least square method to model the
linear dependency between covariate variables and labels. By applying appropriate …

Linear discriminant analysis (LDA) is an important feature extraction method. This paper
proposes an improved linear discriminant analysis method, which redefines the within-class …

Linear Discriminant Analysis (LDA) is a well-known feature extraction method for supervised
subspace learning in statistical pattern recognition. In this paper, a novel method of LDA …

Feature extraction is a key technology of face recognition, and it can directly affect the
performance of the face recognition system. Among them, linear discriminant analysis (LDA) …