Abstract Linear Discriminant Analysis (LDA) is a popular data-analytic tool for studying the
class relationship between data points. A major disadvantage of LDA is that it fails to …

The linear discriminant analysis (LDA) is a very popular linear feature extraction approach.
The algorithms of LDA usually perform well under the following two assumptions. The first …

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 …

The small sample size problem is often encountered in pattern recognition. It results in the
singularity of the within-class scattering matrix S/sub w/in linear discriminant analysis (LDA) …

It is well-known that the applicability of linear discriminant analysis (LDA) to high-
dimensional pattern classification tasks such as face recognition often suffers from the so …

A new LDA-based face recognition system is presented in this paper. Linear discriminant
analysis (LDA) is one of the most popular linear projection techniques for feature extraction …

In this paper an efficient feature extraction method named as locally linear discriminant
embedding (LLDE) is proposed for face recognition. It is well known that a point can be …

The goal of face recognition is to distinguish persons via their facial images. Each person's
images form a cluster, and a new image is recognized by assigning it to the correct cluster …

Abstract Linear Discriminant Analysis (LDA) assumes that all samples from the same class
are independently and identically distributed (iid). LDA may fail in the cases where the …

We present a modular linear discriminant analysis (LDA) approach for face recognition. A
set of observers is trained independently on different regions of frontal faces and each …