Abstract In existing Linear Discriminant Analysis (LDA) models, the class population mean is
always estimated by the class sample average. In small sample size problems, such as face …

The Fisherface method is a most representative method of the linear discrimination analysis
(LDA) technique. However, there persist in the Fisherface method at least two areas of …

In the existing LDA models, class mean vector is always estimated by the class sample
average. In small sample size problems such as face recognition, however, the class sample …

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 Analysis (LDA) is an effective feature extraction method, but there exist
at least two critical drawbacks in it: small sample size problem and rank limitation. In order to …

Discriminant analysis is effective in extracting discriminative features and reducing
dimensionality. In this paper, we propose an optimal subset-division based discrimination …

This paper proposes a novel method, called two-dimensional local graph embedding
discriminant analysis (2DLGEDA), for image feature extraction, which can directly extract the …

Linear discriminant analysis (LDA)-based methods have been very successful in face and
palmprint recognition. Recently, a class of post-processing approaches has been proposed …

Fisher's linear discriminant analysis (LDA) is popular for dimension reduction and extraction
of discriminant features in many pattern recognition applications, especially biometric …

As an efficacy classification technique, linear regression based classification (LRC) has
been popularly used for image recognition. However, since LRC neglects the local structure …